Bed system

ABSTRACT

A bed system includes: an imaging device; a bed on which the imaging device is to be installed; and a controller configured to process an image acquired by the imaging device to predict a possibility of overturning of a user, in which, when it is determined that a state of the user is a first state, the controller predicts the possibility of overturning of the user based on a first parameter, when it is determined that the state of the user is a second state, the controller predicts the possibility of overturning of the user based on a second parameter, the first state is a state of the user different from the second state, and the first parameter is different from the second parameter.

FIELD

The present embodiment relates to a bed system and the like.

BACKGROUND

There has been proposed a system that provides various kinds of supportto a user who uses abed device. For example, an embodiment is disclosedin which an image capturing a bed and a user is input, and a behavior ofdeparting from the bed and a risk that the user falls are determinedbased on the image (for example, see JP-A-2019-008515, andJP-A-2018-067203).

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A to 1C are diagrams illustrating an overall system according toa first embodiment.

FIGS. 2A to 2D are diagrams for illustrating installation situations ofcameras according to the first embodiment.

FIG. 3 is a diagram for illustrating a functional configurationaccording to the first embodiment.

FIGS. 4A and 4B are diagrams for illustrating surrounding environmentacquisition processing according to the first embodiment.

FIGS. 5A and 5B are diagrams for illustrating posture acquisitionprocessing according to the first embodiment.

FIG. 6 is a diagram for illustrating motion acquisition processingaccording to the first embodiment.

FIG. 7 is a diagram for illustrating prediction processing according tothe first embodiment.

FIG. 8 is a diagram for illustrating correspondence execution processingaccording to the first embodiment.

FIG. 9 is a diagram showing an overall system according to a secondembodiment.

FIG. 10 is a diagram for illustrating a functional configurationaccording to the second embodiment.

FIGS. 11A to 11D are diagrams for illustrating arrangement (positions)of camera devices according to a fourth embodiment.

FIG. 12 is a diagram for illustrating an operation example according toa fifth embodiment.

FIGS. 13A and 13B are diagrams for illustrating the operation exampleaccording to the fifth embodiment.

FIG. 14 is a flowchart for illustrating processing according to thefifth embodiment.

FIG. 15 is a diagram for illustrating the operation example according tothe fifth embodiment.

FIGS. 16A and 16B are diagrams for illustrating the operation exampleaccording to the fifth embodiment.

FIG. 17 is a flowchart for illustrating processing according to a sixthembodiment.

FIG. 18 is a diagram for illustrating an operation example according toa seventh embodiment.

FIG. 19 is a flowchart for illustrating processing according to theseventh embodiment.

FIG. 20 is a diagram for illustrating a modification.

DETAIL DESCRIPTION

One or more embodiments are now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the various embodiments. It is evident,however, that the various embodiments can be practiced without thesespecific details (and without applying to any particular networkedenvironment or standard).

As used in this disclosure, in some embodiments, the terms “component,”“system” and the like are intended to refer to, or comprise, acomputer-related entity or an entity related to an operational apparatuswith one or more specific functionalities, wherein the entity can beeither hardware, or a combination of hardware and software in execution.

One or more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software application orfirmware application executed by a processor, wherein the processor canbe internal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software storedon a non-transitory electronic memory or firmware that confers at leastin part the functionality of the electronic components. While variouscomponents have been illustrated as separate components, it will beappreciated that multiple components can be implemented as a singlecomponent, or a single component can be implemented as multiplecomponents, without departing from example embodiments. Further, thevarious embodiments can be implemented as a method, apparatus or articleof manufacture using standard programming and/or engineering techniquesto produce software, firmware, hardware or any combination thereof tocontrol a computer to implement the disclosed subject matter. The term“article of manufacture” as used herein is intended to encompass acomputer-readable (or machine-readable) device or computer-readable (ormachine-readable) storage/communications media having a computer programstored thereon. For example, computer readable storage media cancomprise, but are not limited to, magnetic storage devices (e.g., harddisk, floppy disk, magnetic strips), optical disks (e.g., compact disk(CD), digital versatile disk (DVD)), smart cards, and flash memorydevices (e.g., card, stick, key drive). Of course, those skilled in theart will recognize many modifications can be made to this configurationwithout departing from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Embodiments described herein can be exploited in substantially anywireless communication technology, comprising, but not limited to,wireless fidelity (Wi-Fi), global system for mobile communications(GSM), universal mobile telecommunications system (UMTS), worldwideinteroperability for microwave access (WiMAX), enhanced general packetradio service (enhanced GPRS), third generation partnership project(3GPP) long term evolution (LTE), third generation partnership project 2(3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA),Z-Wave, Zigbee and other 802.XX wireless technologies and/or legacytelecommunication technologies.

In general, one aspect of the present application is a bed systemincludes, an imaging device, a bed on which the imaging device is to beinstalled, and a controller configured to process an image acquired bythe imaging device to predict a possibility of overturning of a user, inwhich, when it is determined that a state of the user is a first state,the controller predicts the possibility of overturning of the user basedon a first parameter, when it is determined that the state of the useris a second state, the controller predicts the possibility ofoverturning of the user based on a second parameter, the first state isa state of the user different from the second state, and the firstparameter is different from the second parameter.

In general, another aspect of the present application is a bed systemincludes, a imaging device, a bed on which the imaging device ismounted, and a controller configured to process an image acquired by theimaging device to predict a possibility of overturning of a user, inwhich, the controller is configured to predict a risk based on aparameter selected based on positions of the bed and the user, and whenit is determined that there is a reduction factor based on the imageacquired by the imaging device, predict the risk at a level lower than apredicted level.

Hereinafter, one embodiment for implementing a system of the presentapplication will be described with reference to the drawings. Contentsof the present application is merely an example of a mode forimplementing the present application, and is not limited to thedisclosed numerical values and configurations, and includes anequivalent scope that can be conceived by a person skilled in the art.

1. First Embodiment

Various methods are proposed to prevent a user from overturning orfalling. For example, a system according to a comparative exampleanalyzes an image in which a bed device and a user are captured. Thesystem according to the comparative example can recognize that the usermay depart from the bed or that the user may fall from the bed device byanalyzing the image. The system according to the comparative examplenotifies a health care worker, a staff, an assistant, or the like whenthere is a high risk of falling of the user.

When a timing of notification to the health care worker, the staff, theassistant, or the like is late, there is a risk that the user falls fromthe bed device. After the user departs from the bed device, there may bea risk that the user falls due to an environment around the bed device(for example, a wheelchair, a position where shoes are placed, and anarrangement of a side table).

However, when the system according to the comparative example makes anotification at a timing earlier than the timing at which the userdeparts from the bed device, a notification is excessively (more thannecessary) made to the health care worker, the staff, the assistant, orthe like, which causes a work burden. A degree of overturning andfalling risk varies depending on a physical situation of the user, adifference in the surrounding environment, or the like. Therefore, afterthe system according to the comparative example sets a notificationcondition, when the staff or the like is notified based on a uniformcriterion according to the setting condition, the staff or the like maynot be able to take an appropriate measure according to characteristicsof the user or the surrounding environment.

According to a first system of the present embodiment, a system capableof predicting the risk of the user, providing the notification at anappropriate timing, and giving appropriate advice is provided.

The user in the present specification refers to a person who uses thebed device (mattress), and is not limited to a person who receivestreatment due to a disease (for example, a patient). A person whoreceives care in a facility or a person who is on the bed device (forexample, a supine person) can be a user even if the person is a healthyperson.

In the present specification, the staff or the like includes not only aperson who assists the user, such as the health care worker, the staffin the facility, or the family, but also a person related to the user.

In the present specification, a term “obstacle” refers to a reason thatcauses overturning and falling of the user. For example, obstacles arethose such as a table, a pole, and the wheelchair placed near the beddevice, and those contained in a living room such as footwear of theuser (shoes and sandals) and a curtain. The obstacle may include anevent. For example, when a wet floor causes the user to overturn andfall, such an event may also be included in the obstacle.

When the bed device itself is a reason that causes the overturning andfalling of the user, the bed device itself may be an obstacle. Forexample, when a direction of casters of the bed device is notappropriate, or when the casters of the bed device are in an unlockedstate, the bed device may be an obstacle. When the direction of thecasters is not appropriate, for example, when the user pushes the beddevice, the casters may be easily moved.

The overturning and falling in the present specification includeoverturning and/or falling. For example, the “reason that causes theoverturning and falling of the user” includes either or both of the“reason that causes overturning of the user” and the “reason that causesfalling of the user”.

In the present specification, an image is an image (image data) capturedby a camera device, and includes all of a still image, a moving image,and other images captured by the camera device.

[1.1 Overall Description]

[1.1.1 Description of Overall System]

FIG. 1A is an overall view for illustrating an outline of a system 1according to the present embodiment. FIG. 1B is a diagram forillustrating a configuration of a bed device 3. FIG. 1C is a diagramillustrating an arrangement of a sensor device 30.

As shown in FIG. 1A, the system 1 includes a control device 10, a serverdevice 50, a terminal device 52, and a mobile terminal device 54. Thecontrol device 10, the server device 50, the terminal device 52, and themobile terminal device 54 are communicably connected via a network NW.

The bed device 3 is provided with one or a plurality of movable backsections, seat sections, upper leg sections, lower leg sections, and thelike (hereinafter, collectively referred to as “section”), and canperform a back raising motion and a foot raising motion. For example,FIG. 1B shows a state in which the back raising motion is performed byraising the back section. The bed device 3 may place a mattress 4 on thesection.

The bed device 3 includes a raising and lowering mechanism, and canchange a height (floor height) of the section of the bed device 3. Inthe present specification, the floor height of the bed device refers toa distance from the floor on which the bed device 3 serving as acriterion is placed to the section. The floor height of the bed device3, in addition to the distance from the floor to the section, may be adistance from the floor to an upper frame or a distance from the floorto the mattress. The floor height of the bed device may be set such thata criterion position is not the floor but a lower frame.

The bed device 3 can also perform an operation of tilting the entire beddevice 3 (tilt operation). The bed device 3 may be a bed device in whichany part is not movable.

The bed device 3 can be detachably installed with a board 5. As shown inFIG. 1B, the board can be installed with a head board (board 5 a) on ahead side, and a foot board (board 5 b) on a foot side. The bed device 3can be detachably installed with a side rail 6. The side rail 6 can beinstalled on the left and right of the bed device 3. A plurality of theside rails 6 can also be installed in a longitudinal direction of thebed device 3. For example, two side rails 6 may be installed on the headside and the foot side of the bed device 3.

The bed device 3 is movable by using casters 7. The caster 7 includes alock mechanism. When the bed device 3 is locked by the lock mechanism,movement of the bed device 3 is restricted.

The bed device 3 further includes a camera device 20. The camera device20 captures an image of a user P, the bed device 3, a state of thesurrounding environment, the staff, and the like. A method of installingthe camera device 20 will be described later.

Here, the “surrounding environment” refers to an environment of asurrounding range where the bed device 3 is installed (for example, arange included in a predetermined distance from a position where the beddevice 3 is installed, a range including a living room or a hospitalroom where the bed device 3 is present, or a range where the cameradevice 20 can capture the image). For example, the surroundingenvironment includes an object arranged around the bed device 3 (forexample, a bed head base, and an IV pole), a movable object such as theshoes or the wheelchair, room brightness, and a position of anotheruser, the staff, or the like in a case of a multi-bed room.

In the present specification, the “surrounding” refers to a range (angleof view) that can be captured by the camera device 20. For example, whenthe camera device 20 can capture an image of a doorway of the livingroom in which the bed device 3 is installed or an entire range of theliving room, the doorway of the living room or the entire range of theliving room is also included in the “surrounding”.

The sensor device 30 can be placed on the bed device 3. The sensordevice 30 is a sensor that detects a body motion of the user who is onthe bed device 3. By using the sensor device 30, the control device 10can acquire a biological information value such as a heartbeat rate anda respiratory rate of the user, and can detect a position, a center ofgravity, a posture, and the like of the user.

The sensor device 30 is a sensor capable of detecting a state of theuser and a state of the living room and a hospital room. For example,the sensor device 30 may be placed on the mattress 4 on the bed device3, or may be placed between the section of the bed device 3 and themattress 4. The position where the sensor device 30 is placed may be aposition where the body motion of the user can be detected on the beddevice 3. The position where the sensor device 30 is placed ispreferably a position corresponding to the back of the user when theuser lies down.

For example, as shown in FIG. 1C, the sensor device 30 is placed on orbelow the mattress 4 placed on the section of the bed device 3. At thistime, the sensor device 30 is placed at a position away from a head sideend by a distance M20 so as to be located on the back (near the chest)of the user P. For example, M20 is 40 cm.

The sensor device 30 may be implemented by providing a load sensor belowthe bed device 3 (between the floor and the bed device 3), or may detectthe body motion of the user by providing a strain gauge on a frame or amotor supporting the bed.

The sensor device 30 may include other sensors. For example, the sensordevice 30 may be an illuminance sensor that detects the brightness ofthe living room, a position sensor that detects the position of anotherdevice, or a sensor that performs face authentication of the user. Thesensor device 30 may be an odor sensor that detects excretion of theuser. For example, an excretion sensor disclosed in JP-A-2019-178890(Title of invention: Excretion sensor, Filing date: Mar. 30, 2018) canbe used. The patent application is incorporated by reference in itsentirety. The sensor device 30 may be any device capable of detectingany information or state.

Equipment is placed as various objects in the living room or thehospital room where the bed device 3 is present. For example, equipmentsuch as a stand (IV stand) to which an infusion or the like can beattached, a pole (IV pole) attached to the bed device 3, the side table,the wheelchair, and a cabinet (bed head base) may be placed. The system1 may use the camera device 20 to grasp the positions of the equipmentand objects, as will be described later. The system 1 may grasp theposition and the state of the equipment such as the IV stand, the IVpole, the side table, the wheelchair, and the cabinet by respectivelyincorporating an IoT unit (including a communication module) therein.

Further, curtains (a curtain around the bed device 3, a curtain attachedto a window, and the like) in the living room and the hospital room maybe used as the equipment. The system 1 may grasp whether the curtain isopen or closed by using the camera device 20 or the IoT unit. The system1 may use the camera device 20 or the IoT unit to grasp whether thecurtain installed surrounding the bed is open or closed. The curtaininstalled surrounding the bed may be, for example, a curtain forpartitioning a space of each patient in a multi-bed room.

The control device 10 is a device that controls the overall system. Thecontrol device 10 according to the present embodiment is installed in atablet device, and may control each device from another device. Forexample, the control device 10 may display the biological informationvalues such as the heartbeat rate and the respiratory value acquired bythe sensor device 30 on a monitor.

The control device 10 may be further connected to another device. Forexample, the control device 10 may be connected to a bed control device40 (FIG. 3 ) that controls the bed device 3. The control device 10 canacquire a state of the bed device 3 and control the bed device 3 (forexample, control of back raising/back lowering).

The control device 10 may be constituted integrally with the cameradevice 20, or may be constituted as a device different from the cameradevice 20. The control device 10 may be integrated with the bed controldevice 40 that controls the bed device 3.

The control device 10 may be implemented by a device in another place,or may be provided by installing an application in the terminal device(for example, a smartphone) of the user, the staff or the like.Functions implemented by the control device 10 may be implemented on theserver device side.

The control device 10 is constituted to be connectable to the networkNW. For example, the control device 10 is connected to an access point12 via a wireless LAN, and is connected to the network NW. The accesspoint 12 is a base station device of the wireless LAN, and is capable ofwireless communication such as IEEE 802.11a/b/g/n. The control device 10may perform communication using a wired LAN, short-distance wirelesscommunication of Bluetooth (registered trademark), or anothercommunication line such as LTE/5G, instead of the wireless LAN.

The server device 50 is a server that manages information necessary forthe system. The server device 50 manages various kinds of informationsuch as information related to a disease of the user, informationrelated to administration, and information related to hospitalizationhistory, and overturn history. The terminal device 52 and the mobileterminal device 54 are devices used by the staff. For example, whenthere is a risk of overturning and falling, the control device 10 maynotify the terminal device 52 or the mobile terminal device 54.

The server device 50 may receive and manage data acquired by the controldevice 10. For example, the server device 50 may receive and manage animage captured by the camera device 20. The server device 50 is notlimited to a server device that manages the present system, and includesa server device that manages a medical system and hospital system, anelectronic medical record server device, and a management server devicecustomized by the hospital or the facility.

[1.1.2 Pattern of Camera]

FIGS. 2A to 2D are diagrams for illustrating patterns in which camerasare used. Hereinafter, five patterns will be described. The cameradevice 20 may be built in or externally attached to the frame of the beddevice 3 or the board 5 of the bed device 3 in advance. The cameradevice 20 may be built in or externally attached to the bed device 3later.

(Pattern a)

As illustrated in FIG. 2A, the camera devices 20 are installedsurrounding the bed device 3. Specifically, the camera devices 20 areinstalled in frames on the head side, the foot side, and longitudinalsides (a right hand side of the user and a left hand side of the user)of the bed device 3, the board 5, a fence attached to the bed (forexample, an insertion fence, a folding fence, a tracking fence, which isalso referred to as the side rail for convenience of description) and anassistance bar, and other dedicated attachment tools using a fence holeor the board. The camera device 20 can capture an outer periphery of thebed device 3, the user, the staff, and the like. For example, the cameradevice 20 may be a device that can capture an image preferably in arange of 120 to 230 degrees.

Although the camera devices 20 are installed at the center of the framesand the boards 5 in FIG. 2A, the camera device 20 may be installed atany one of four corner locations of the bed device 3, may be installedat each of four corner locations of the bed device 3, or a plurality ofcamera devices 20, which is two or more on one side, may be installed.The camera device 20 may be installed in combination with one having adifferent angle of view, or may be installed regardless of the place. Alens of the camera device 20 is installed so as to face the outside ofthe bed device 3.

(Pattern b)

As illustrated in FIG. 2B, the camera device 20 is installed on theboard 5. Specifically, the camera device 20 is installed at the centerof the foot board on the foot side (board 5 b) (or the head board on thehead side (board 5 a)) of the bed device 3, so that a full body of theuser on the bed device 3 can be captured. The camera device 20 may be adevice that can capture an image preferably in a range of 60 to 230degrees. Two or more camera devices 20 may be installed, or the cameradevices 20 having different angles of view may be installed incombination. The camera device 20 may be installed at any place, forexample, at the right side or the left side of the foot board (headboard), as long as the camera device 20 is installed in a range in whichthe top of the bed device 3 including the user can be captured. The lensof the camera device 20 is installed so as to face the inside of the beddevice 3.

(Pattern c)

As illustrated in FIG. 2C, the camera devices 20 are installed on theboards 5 on both sides. Specifically, the camera device 20 is installedat the center of the foot board (board 5 b) and the head board (board 5a) of the bed device 3, so that the full body of the user on the beddevice 3 can be captured. Other descriptions are the same as those ofthe second pattern, and detailed descriptions thereof will be omitted.

(Pattern d)

As shown in FIG. 2D, the camera devices 20 are installed at ends of thehead board (board 5 a) and/or the foot board (board 5 b). For example,the camera device 20 is installed in the vicinity of the end of the footboard or in the vicinity of a grip portion provided above the footboard. In this manner, the camera device 20 is arranged at any placewhere the user and the periphery of the bed device 3 can be captured.

In this way, the camera device 20 is provided so as to correspond to onebed device 3, instead of a camera device that is present in a room inadvance. Accordingly, the camera device 20 does not need to performcalibration on the bed device 3, the surrounding environment, or thelike, and convenience of the user, the staff, or the like is improved.Even when the user of the bed device 3 is changed or the living room inwhich the bed device 3 is arranged is changed, since the bed device 3and the camera device 20 correspond to each other, it is not necessaryto change the data stored in the system, and the convenience of theuser, the staff, and the like is improved. For example, since it is notnecessary to perform the calibration, even when the staff or the likeforgets to perform the calibration, it is possible to appropriately usethe camera.

(Pattern e)

In the above patterns, the camera device 20 is installed in the beddevice 3 as an example. However, for example, a camera device 20 alreadyinstalled in the living room or a camera device 20 installed in theequipment can be used. Specifically, when a monitoring camera or thelike is already installed, an image thereof may be used. In this case, acamera device 20 at a position where the user P on the bed device 3 iswithin the angle of view is used.

The patterns a to e described above can be installed in combination asnecessary. For example, an installation pattern can be a combination ofthe pattern a and the pattern c, or a combination of the pattern c andthe pattern d.

As for the pattern using the camera, the camera device 20 may beinstalled at each position later (installed afterwards), or may beinstalled in advance (installed in an initial state). It is also assumedthat the pattern using the camera includes selecting and using a camerafrom the already set cameras. For example, the cameras are installed inadvance at the locations of FIGS. 2A, 2C, and 2D. At this time, in thecase of the pattern c, the system 1 does not use the cameras shown inFIG. 2A or 2D, but uses the cameras installed in FIG. 2C (board 5).

[1.1.3 Acquirable Data by Camera Device]

The control device 10 can acquire the following data by analyzing theimage captured by the camera device 20. Hereinafter, an overview ofacquirable data will be described.

(1) Behavior of User

For example, as a behavior of the user, it is possible to acquire anaction of taking a meal (including a meal amount), water or the like(including a water intake amount), the number of times of rolling over,apnea, a frequency of use of a portable toilet, presence or absence of aprohibited behavior, an abnormal behavior, whether to take medicine, andpresence or absence of a scratching behavior.

(2) State of User

For example, it is possible to acquire, as a state of the user, a changein a physical condition of the user, a facial expression of the user, anemotion of the user, concussion of the user, bleeding of the user,biological information of the user, a sleeping posture of the user,bed-departure and on-bed of the user (including duration of departingfrom the bed or while on the bed), a situation when the user falls, andthe like.

(3) Detection of Person

For example, it is possible to detect and recognize a user, a staff inthe hospital and the facility, a family member, others, and a thirdparty in the capturing range.

(4) Other Information

For example, it is possible to recognize medical record information, aremaining amount of infusion used by the user, a remaining amount ofconsumables used by the user or in the living room, presence or absenceof bed wiping, a position of an auxiliary tool, a state of an object oran obstacle around the bed device 3 or below the bed device 3, a stateof a floor (whether or not the floor is wet), a state of an article(whether or not it is appropriate for the situation where the object isplaced, whether or not the object is broken, or the like), a position ofwiring such as a cable, a behavior of the staff such as the nurse or thefamily member (treatment performed by the staff or the like, forgettinga power supply of the living room or the device), a time point when thestaff or the like visits the user, an unvisited duration time, aposition or the presence or absence of use of equipment in the livingroom, a scene of the entire living room, presence or absence of violenceagainst the user and the staff, presence or absence of theft in theliving room, a disaster such as leakage of water or fire (includinginformation on an occurrence place of a disaster or the like), and thelike.

The control device 10 may acquire data to be acquired from the capturedimage by analyzing and recognizing the image as described later, or mayacquire the data by artificial intelligence (AI) based on the capturedimage.

For example, the system 1 (the control device 10) accumulates, in adatabase, images captured including the inside of the living roomincluding the bed device 3 and the user. At this time, the controldevice 10 causes the neural network to learn a relationship between thecaptured images and information corresponding to the behavior of theuser, the state of the user, the person to be detected, and other imagesby deep learning.

Then, the control device 10 can acquire necessary data to be describedlater by using the learned neural network based on the captured imageincluding the inside of the living room including the bed device 3 andthe user.

[1.1.4 Input Parameters and Output Parameters]

The control device 10 can obtain an output by inputting some parametersto a learned model or a neural network subjected to machine learning.Hereinafter, parameters input by the control device 10 and parametersoutput by the control device 10 will be described based on somesituations.

(1) First Situation

The control device 10 recognizes how much water the user takes in whileon the bed based on the image captured by the camera device 20, predictsbed-departure based on a recognition result, and notifies the staff orthe like of a recommendation of “please guide the user to the toilet”.

In this case, the control device 10 inputs “water intake amount” and“duration on bed” as input parameters. The control device 10 outputs“bed-departure prediction” and “recommendation notification” as outputparameters.

(2) Second Situation

The control device 10 notifies a priority and a necessary preparationitem (article) of a patient who is the user to be circulated by thestaff based on a content of treatment previously performed by the staff(for example, the medical treatment performed by the staff on the user)and an elapsed time (unvisited duration) thereafter.

In this case, the control device 10 inputs “previously performedtreatment” and “unvisited duration” as the input parameters. The controldevice 10 outputs “circulation priority notification” and “necessarypreparation item (article) during visiting” as the output parameters.

(3) Third Situation

When a fire (camera) or an earthquake (vibration) occurs, the controldevice 10 notifies the patient who is the user or the staff in thevicinity of the fire or the earthquake or the user or the staff who isaway from the fire or the earthquake of an alert. The control device 10notifies a different type of alert according to a notificationdestination. For example, the user or the staff in the vicinity of anoccurrence place of a disaster is notified of an alert for evacuation.The user or the staff in a place away from the occurrence place isnotified of an alert for standby.

The control device 10 inputs “fire and earthquake”, “occurrence place”,and “whether or not the user is on the bed” as the input parameters. Thecontrol device 10 outputs “disaster alarm” as the output parameter

(4) Fourth Situation

The control device 10 issues a notification for preventing the stafffrom forgetting the treatment. For example, the control device 10 issuesa notification for preventing a case where a plurality of medicines haveto be administered to the user according to a medical record but a partof the medicines are not administered. The treatment of the staff mayinclude not only the treatment for the user (for example, confirmationof infusion or wiping) but also the treatment of the staff themselves(for example, sanitizing hands of the staff).

The control device 10 inputs “staff treatment” and “medical recordinformation” as the input parameters. The control device 10 outputs a“treatment forgetting alert” as the output parameter.

(5) Fifth Situation

The control device 10 issues the notification according to the state andthe motion of the user. For example, when the user is a paralyzedpatient, the control device 10 reads from the motion and notifies whatthe user cannot do even though he/she wants to (request).

The control device 10 inputs “posture”, “motion”, and “medical recordinformation” as the input parameters. The control device 10 outputs“request estimation (notification)” as the output parameter.

(6) Sixth Situation

The control device 10 performs treatment necessity determination basedon an overturn process of the patient (for example, which part is hitwith what degree of strength).

The control device 10 inputs a “hit part”, an “overturn speed”, and an“impact sound” as the input parameters. The control device 10 outputs“treatment necessity determination” as the output parameter.

(7) Seventh Situation

The control device 10 recommends changes in the surrounding equipment(environment) in accordance with changes in sensor setting. For example,when a setting of a bed-departure sensor is changed from “getting up” toa “sitting position” (that is, when it becomes possible to stand up),the control device 10 recommends not the currently used insertion fencebut the assistance bar.

The control device 10 inputs “sensor setting change (operation of anurse)”, “surrounding equipment”, and the “medical record information”as the input parameters. The control device 10 outputs “equipment andlayout change recommendation” as the output parameter.

(8) Eighth Situation

The control device 10 detects the motion of the user. When there is achange in the physical information described in the medical recordinformation (for example, a decrease/increase of the ADL), the controldevice 10 recommends the change in the surrounding equipment(environment) in accordance with the change.

The control device 10 inputs the “motion”, the “surrounding equipment”,and the “medical record information” as the input parameters. Thecontrol device 10 outputs the “equipment and layout changerecommendation” as the output parameter.

(9) Ninth Situation

The control device 10 notifies the staff when the camera captures anevent in which an object falls from a position where it is supposed tobe present and detects the motion of the user trying to pick up theobject.

The control device 10 inputs “dropping of object”, “hand stretching outaction”, and the “medical record information” as the input parameters.The control device 10 outputs a “fallen object notification (overturnprevention)” as the output parameter.

[1.2 Description of Functional Configuration and Processing]

Next, in the system 1 according to the present embodiment, a functionalconfiguration centered on the control device 10 will be described withreference to FIG. 3 .

[1.2.1 Controller and Storage Unit]

The control device 10 includes a controller 100, a storage unit 120, adisplay 130, a user interface device 135, a notifying unit 140, and acommunicator 150. The communicator 150 can be connected to the cameradevice 20, the sensor device 30, the bed control device 40, the serverdevice 50, and a peripheral device 60.

The controller 100 controls the overall control device 10. Thecontroller 100 is one or a plurality of calculation units (for example,central processing units (CPUs)) that implement various functions byreading and executing various programs stored in the storage unit 120.

The controller 100 functions as an acquisition unit including asurrounding environment acquisition unit 102, a posture acquisition unit104, a motion acquisition unit 106, a biological information acquisitionunit 108, and a disease information acquisition unit 110 by reading andexecuting the programs stored in the storage unit 120. The controller100 functions as a prediction unit 112 and a correspondence executionunit 114.

The storage unit 120 stores various programs and various kinds of datanecessary for the operation of the control device 10. The storage unit120 includes, for example, a solid state drive (SSD) which is asemiconductor memory, and a hard disk drive (HDD).

The storage unit 120 includes a surrounding environment storage area1202, a user information storage area 1204 for storing user information,and an image storage area 1240, and stores an explanatory variable table1220 and a prediction dictionary DB 1230.

Hereinafter, functions implemented by the controller 100 will bedescribed. Here, when the controller 100 functions as the acquisitionunit, each acquisition unit outputs a feature amount as a parameterusable by the prediction unit 112 based on a predetermined input value.The acquisition unit can output one or a plurality of feature amountsdescribed below in combination.

(1) Surrounding Environment Acquisition Unit (First Acquisition Unit)

The surrounding environment acquisition unit 102 acquires an obstaclefrom the equipment included in the image, and acquires the surroundingenvironment of the bed device 3 such as the surrounding brightness,based on the image captured by the camera device 20. The surroundingenvironment acquisition unit 102 outputs and stores the feature amountbased on the acquired surrounding environment in the surroundingenvironment storage area 1202.

Surrounding environment acquisition processing executed by thesurrounding environment acquisition unit 102 will be described withreference to FIG. 4A. First, the surrounding environment acquisitionunit 102 acquires an image from the camera device 20 (step S1002).Specifically, when the control device 10 periodically acquires the imagefrom the camera device 20, the controller 100 stores the received imagein the image storage area 1240. In this case, the surroundingenvironment acquisition unit 102 reads and acquires the image from theimage storage area 1240 in step S1002. Without being limited thereto,for example, when the control device 10 controls the camera device 20 toreceive the image, the surrounding environment acquisition unit 102 maydirectly acquire the image from the camera device 20 in step S1002.

It is preferable that the surrounding environment acquisition unit 102uses the camera device 20 arranged in the pattern a and the pattern d.When the surrounding environment acquisition unit 102 acquires the stateof the bed device 3 (the state of back raising or the like), thesurrounding environment acquisition unit 102 may use the camera device20 arranged in the pattern b or the pattern c.

The surrounding environment acquisition unit 102 recognizes the state ofthe bed device 3 based on the image (step S1004). Here, the state of thebed device 3 means a situation of a movable portion when at least a partof the bed device 3 is movable.

The surrounding environment acquisition unit 102 analyzes the image andrecognizes items such as the height (floor height) of the section of thebed device 3, a back raising angle, an upper leg raising angle, aninclination angle (tilt angle), and whether or not the back section andthe upper leg section operate in conjunction with each other as thestate of the bed device 3. For example, the surrounding environmentacquisition unit 102 outputs “20 cm” as the floor height of the beddevice 3, “20 degrees” as the back raising angle, and “10 degrees” asthe upper leg raising angle as one of the feature amounts.

The surrounding environment acquisition unit 102 analyzes the image torecognize the obstacle (step S1006). When the surrounding environmentacquisition unit 102 recognizes the obstacle as the surroundingenvironment, the surrounding environment acquisition unit 102recognizes, as the obstacle, the equipment or the object that may causeoverturning and falling among the equipment or the object included inthe image. Then, the surrounding environment acquisition unit 102recognizes a type, a position, and a direction of the obstacle, whetheror not the obstacle is on a route, a size, a shape, presence or absenceof the motion, a distance from the bed device 3, and the like asnecessary. The surrounding environment acquisition unit 102 outputsinformation related to the recognized obstacle as one of the featureamounts.

For example, the surrounding environment acquisition unit 102 outputs asthe feature amounts that the type of the obstacle is the “wheelchair”,the size thereof is “56 cm×100 cm” and the position is at “30 cm on theright side of the bed device 3”.

The surrounding environment acquisition unit 102 may recognize thedirection of the casters 7 of the bed device 3, a lock state, presenceor absence of the side rail (presence or absence of installation,presence or absence of use), and the presence or absence of theassistance bar (presence or absence of installation, presence or absenceof use) as the state of the bed device 3 or the obstacle. Thesurrounding environment acquisition unit 102 outputs a recognitionresult as the feature amount.

Although the surrounding environment acquisition unit 102 acquires thesurrounding environment by recognizing the image of the camera device 20in the above description, the surrounding environment acquisition unit102 may acquire the surrounding environment from other than the image.For example, the surrounding environment acquisition unit 102 maydirectly acquire the back raising angle, the upper leg raising angle,the floor height, and the like of the bed device 3 from the bed controldevice 40 and output the acquired information as the feature amount. Thesurrounding environment acquisition unit 102 may acquire the type, theposition, the size, and the like of the obstacle by communicating withthe peripheral device 60 provided in the obstacle.

The surrounding environment acquisition unit 102 may selectively executesteps S1004 to S1006 in FIG. 4A. For example, only one of the threesteps may be executed, or two of the three steps may be executed. Thesurrounding environment acquisition unit 102 may execute the steps inany combination.

FIG. 4B is a diagram illustrating an example of a first feature amountrelated to the surrounding environment stored in the surroundingenvironment storage area 1202. For example, the surrounding environmentstorage area 1202 stores the feature amount of the state of the beddevice 3 and the feature amount based on the obstacle.

The surrounding environment storage area 1202 is capable of storing, asfeature amounts related to the bed device 3, the floor height, the backraising angle, the upper leg raising angle, a foot raising angle, theinclination angle (tilt angle), and whether or not the back section andthe upper leg section operate in conjunction with each other.

When recognizing the state of the bed device 3, the surroundingenvironment acquisition unit 102 may also recognize the state of a thingthat is used in association with the bed device 3 (for example, an airmat). The surrounding environment storage area 1202 may store the stateof the operation of the bed device 3 as the feature amount. For example,when the back section and the upper leg section of the bed device 3 arein conjunction, the surrounding environment storage area 1202 may storea flag “1” as the feature amount. When a rolling operation is performedin the bed device 3, the flag “1” may be stored as the feature amount.

The surrounding environment storage area 1202 may store equipment or thelike mounted on the bed device 3. For example, the presence and absenceof the board 5, the presence and absence of the side rail 6, and thestate of the casters 7 may be stored. The surrounding environmentstorage area 1202 may store the presence or absence of the headboard/foot board as the board 5, and a place where the side rail 6 ismounted (for example, the right side, the left side, the upper side(head side), and the lower side).

The surrounding environment storage area 1202 stores the type of theobstacle, the position of the obstacle, and the like as the featureamount related to the obstacle. The surrounding environment storage area1202 may store the position of the obstacle in terms of the distancefrom the bed device 3 or the camera device 20 as the feature amount, ormay store relative position coordinates (for example, XYZ coordinates inwhich the living room is a virtual space). The surrounding environmentstorage area 1202 may store the feature amount corresponding to the“assistance bar”, the “table”, or the like, which is the type of theobstacle, or may store the size of the obstacle itself (for example,width x cm, length y cm, and height z cm) as the feature amount.

The surrounding environment storage area 1202 may store the featureamount in the user information storage area 1204 in association with anID of the user or a time point.

(2) Posture Acquisition Unit (Second Acquisition Unit)

The posture acquisition unit 104 acquires the posture of the user fromthe image captured by the camera device 20, and outputs the featureamount. Here, the posture of the user refers to a way of holding thebody of the user, and is estimated based on the image. The postureacquisition unit 104 may acquire not only the way of holding the body ofthe user but also, for example, the position of the user (whether or notthe user departs from the bed) and the place (whether or not the user isat the sitting position) as the posture to be estimated. “Acquiring theposture” refers to specifying a posture based on feature points of auser as described later, and also includes a concept of estimating theposture of the user based on a part of the feature points.

The posture acquisition unit 104 may store the acquired posture of theuser in the user information storage area 1204 in association with theuser together with an acquisition time point.

Then, the posture acquisition unit 104 outputs the feature amount basedon the acquired posture. The feature amount may be a feature amount foreach posture such as “1” in the case of the sitting position, “2” in thecase of a supine position, and “0” in other cases, or may be an angle ofthe direction of the user (for example, “10 degrees to the right”), aposition of the head (for example, “+5 degrees” with respect to avertical direction), or a way of holding the body of the user (forexample, “angle of raising the right hand”). The posture acquisitionunit 104 may output “1” as the feature amount in a case where allpostures or the way of holding the body is set as an attribute.

The posture acquisition unit 104 stores a second feature amount based onthe acquired posture of the user in the user information storage area1204 in association with each user.

Posture acquisition processing executed by the posture acquisition unit104 will be described with reference to FIG. 5A. First, the postureacquisition unit 104 acquires an image (step S1102). The postureacquisition unit 104 can acquire the image by using the same method asthat used by the surrounding environment acquisition unit 102 to acquirethe image.

The posture acquisition unit 104 recognizes a skeleton part of the useras the feature point based on the acquired image (step S1104). Forexample, the posture acquisition unit 104 recognizes a position of ashoulder, a position of a face, a position of a hand, and a position ofa foot of the user, and also recognizes a position of a joint such as anupper leg joint or an elbow joint by pattern image recognition.

FIG. 5B is a diagram schematically illustrating the skeleton part of theuser. For example, the posture acquisition unit 104 recognizes thecaptured skeleton part of the user by pattern image analysis, andrecognizes each feature point of the user. Then, the posture acquisitionunit 104 can recognize the skeleton of the user by detecting a lineconnecting the recognized feature points of a human body.

The posture acquisition unit 104 acquires the size of the user, theposture such as the direction of the body of the user (including thedirection of the body, the direction of the face, and the direction of aline of sight), and the way of holding the body of the user by using thefeature points (step S1106). In the acquisition of the posture of theuser, for example, the posture may be acquired by performing coordinateregression analysis on the feature points, or the posture may beacquired using a result of machine learning. Then, the postureacquisition unit 104 outputs the second feature amount based on theacquired posture.

Although the posture acquisition unit 104 is described as acquiring theposture of the user by recognizing the feature points based on thecaptured image, another method may be used. For example, the posture ofthe user may be directly acquired from the image by using a neuralnetwork.

A person or a posture of the person in the image may be recognized fromthe image by an Instance Segmentation method. Specifically, the postureacquisition unit 104 recognizes an area of the object included in theimage (the person, the bed device 3, or the like) or an area in which apart of the person is present as the object. That is, the postureacquisition unit 104 may acquire the posture of the user by recognizingan area including the person or recognizing an area of the hand, thefoot, or the like of the person.

A method of acquiring the posture or the like of the user other than theabove may be used. For example, a method for detecting a state of a userdisclosed in JP-A-2009-118980 (Title of invention: State detectionsystem for user in bed, Filing date: Nov. 13, 2007), and a method fordetecting a position of a user disclosed in JP-A-2008-206869 (Title ofinvention: Bed device, Filing date: Feb. 27, 2007) can be incorporated.The patent application is incorporated by reference in its entirety.

In the above description, the posture acquisition unit 104 outputs thefeature amount based on the posture of the user based on the imagecaptured by the camera device 20. However, in addition to that, theposture acquisition unit 104 may acquire the posture of the user andoutput the feature amount by using, for example, the sensor device 30.

The posture acquisition unit 104 may acquire a facial expression fromthe face of the user by analyzing the image. When the facial expressionis acquired, the posture acquisition unit 104 outputs the feature amountbased on the facial expression.

(3) Motion Acquisition Unit (Third Acquisition Unit)

The motion acquisition unit 106 acquires the motion of the user based oninformation (sensor information) acquired by the camera device 20 or thesensor device 30, and outputs the feature amount based on the motion ofthe user. Here, the motion of the user refers to a movement of the bodyof the user. Examples of the motion of the user include a movementamount of the user, a movement of the hand or foot of the user, aposition of a center of gravity (including a movement of the position ofthe center of gravity), wobbling, gait, behavior, and rolling over. Themotion of the user includes not only the motion such as standing up butalso a speed of standing up, a walking speed, a direction of a directionchange, and a time related to the direction change. The motionacquisition unit 106 may store the acquired motion of the user in theuser information storage area 1204 in time series for each user.

Motion acquisition processing executed by the motion acquisition unit106 will be described with reference to FIG. 6 . First, the motionacquisition unit 106 acquires the sensor information from the cameradevice 20 or the sensor device 30 (steps S1202 and S1204). The sensorinformation output by the sensor device 30 is, for example, vibrationdata. The motion acquisition unit 106 can acquire the motion of the userby analyzing the vibration data or the image (step S1206).

Here, the information acquired by the motion acquisition unit 106 fromthe camera device 20 is an image. The motion acquisition unit 106estimates the posture of the user in time series based on the image, andacquires the motion of the user such as the position of the center ofgravity, the wobbling, the gait, and the behavior of the user. Theinformation to be acquired by the motion acquisition unit 106 from thesensor device 30 in order to acquire the motion of the user is, forexample, a load change amount on the bed, data on center-of-gravity,vibration data of air pressure (including body motion data, heartbeatdata, and respiratory data which are vital data of the user). That is,the motion acquisition unit 106 can acquire the motion of the user basedon the information acquired from the sensor device 30.

Then, the motion acquisition unit 106 outputs a feature amount based onthe acquired motion as the feature amount, and stores the feature amountin the user information storage area 1204. Here, what is stored as thefeature amount based on the motion is, for example, as follows.

-   -   When it is acquired that the body motion of the user is large,        “1” is stored as the feature amount.    -   When it is acquired that the user (or a part of the body of the        user) moves a predetermined distance or more, “1” is stored as        the feature amount.    -   When it is acquired that the load change is equal to or greater        than a threshold value, “1” is stored as the feature amount. A        numerical value of the load change is stored as the feature        amount.    -   The number of times of rolling over is stored as a feature        amount.

When there is no change in the motion of the user, the motionacquisition unit 106 may store the fact that there is no change in themotion of the user in the user information storage area 1204 as thefeature amount. That is, the motion acquisition unit 106 may acquire themotion of the user periodically (for example, every 1 second, 5 seconds,1 minute, or 5 minutes) and store a third feature amount based on themotion in the user information storage area 1204.

(4) Biological Information Acquisition Unit (Fourth Acquisition Unit)

The biological information acquisition unit 108 acquires biologicalinformation based on the information acquired by the sensor device 30.For example, the biological information acquisition unit 108 calculatesa respiratory waveform or a heartbeat waveform based on the body motionreceived from the sensor device 30 by executing biological informationacquisition processing. Then, the biological information acquisitionunit 108 acquires a respiratory rate from the calculated respiratorywaveform and a heartbeat rate from the calculated heartbeat waveform asvalues of the biological information. The biological informationacquisition unit 108 calculates a fourth feature amount based on thebiological information value and outputs the fourth feature amount.

As a method of acquiring the biological information such as therespiratory rate and the heartbeat rate, for example, a method disclosedin JP-A-2016-30177 (Title of invention: Respiratory disorderdetermination device, respiratory disorder determination method andprogram, Filing date: Jul. 30, 2014) can be incorporated. Other knowntechniques may be used.

In addition to the respiratory rate and the heartbeat rate, thebiological information acquisition unit 108 can also acquire values ofthe biological information such as body temperature, blood pressure, andtransdermal arterial oxygen saturation (SpO2). The biologicalinformation acquisition unit 108 may acquire the biological information(for example, a pulse value or the respiratory rate) that can becontinuously acquired by the sensor device 30, or may acquire any of thebiological information that can be discretely (in a spot manner)acquired by an external device or the like, such as the blood pressuremeasured by a sphygmomanometer or the body temperature measured by athermometer.

The biological information acquisition unit 108 may store the biologicalinformation value as it is in the user information storage area 1204 foreach user as the feature amount. The biological information acquisitionunit 108 may acquire that the biological information value is, forexample, a normal level, a caution level, or a warning level, and mayoutput the feature amount according to the level.

The biological information acquisition unit 108 may acquire informationrelated to the user that can be acquired based on the biologicalinformation. For example, the biological information acquisition unit108 may acquire a sleep state or an awake state of the user, and mayfurther acquire REM sleep and non-REM sleep as the sleep state. Thebiological information acquisition unit 108 may output “1” as thefeature amount of sleep when the user is in the sleep state and “0” asthe feature amount of sleep when the user is in the awake state.

As a method of determining the sleep state of the users, for example,methods disclosed in JP-A-2010-264193 (Title of invention: Sleep statedetermination device, program, and sleep state determination system,Filing date: May 18, 2009) and JP-A-2016-87355 (Title of invention:Sleep state determination device, sleep state determination method, andprogram, Filing date: Nov. 11, 2014) can be incorporated. The patentapplication is incorporated by reference in its entirety. Other knownmethods may be used to acquire the sleep/awake state of the user.

The biological information acquisition unit 108 may acquire thebiological information of the user from the camera device 20. As thebiological information that can be acquired by the biologicalinformation acquisition unit 108, for example, the facial expression orthe line of sight of the user, and the body motion (for example,restless legs syndrome during sleep, or the like) can be acquired. Thebiological information acquisition unit 108 can detect body motion andacquire respiration and heartbeat by performing image analysis on theimage. The biological information acquisition unit 108 can acquire thebody temperature by using a thermal infrared camera device as the cameradevice 20.

(5) Disease Information Acquisition Unit (Fifth Acquisition Unit)

The disease information acquisition unit 110 acquires informationrelated to a disease of the user (disease information). For example,when the disease information acquisition unit 110 executes diseaseinformation acquisition processing, the disease information acquisitionunit 110 is connected to the server device 50 and accesses an electronicmedical record DB 502. Then, the disease information acquisition unit110 acquires the information on the disease of the user. Here, thedisease information includes, in a broad sense, information related tothe disease of the user, and includes not only information related to asimple disease state, but also information related to administration,information related to operation, information related to surgery,information such as hospitalization history, information such as havingparalysis of hands and feet, precautions in meals, whether or not anauxiliary tool is used, and precautions related to the user.

The disease information acquisition unit 110 stores the feature amountbased on the disease information in the user information storage area1204. The feature amount acquired by the disease information acquisitionunit 110 may be, for example, a feature amount based on a diseasehistory, a medical history, or an admission history. The diseaseinformation may include an overturn history. The staff or the like mayinput the overturn history, or the system may detect the number of timesthe user overturns and automatically update the overturn history. Thefeature amount acquired by the disease information acquisition unit 110may be a feature amount based on medicine taking information, healthcheckup information, records of various kinds of assessment (includingoverturn assessment), and the like of the user.

The disease information acquisition unit 110 may output the number ofhospitalization days as a fifth feature amount. The disease informationacquisition unit 110 may output “1” as the fifth feature amount when thedisease corresponds to a specific disease, or may calculate and outputthe fifth feature amount based on a blood pressure value or a bloodglucose value of the user.

(6) Prediction Unit

The prediction unit 112 uses the prediction dictionary DB 1230, which isa prediction model, to predict the risk of overturning and falling. Theprediction unit 112 may predict a probability or a future time point atwhich overturning and falling may occur as the possibility of theoverturning and falling. The prediction dictionary DB 1230 is dictionarydata of a learned model generated by any one of the machine learningmethods.

The prediction unit 112 inputs the above-described feature amount to anartificial intelligence program as an explanatory variable. Theartificial intelligence program uses the prediction dictionary DB 1230to output, as an objective variable, the risk (possibility) of the useroverturning and falling.

In the example described above, although the prediction unit 112 outputsthe risk (possibility) of the user overturning and falling based on aplurality of feature amounts, the present embodiment is not limitedthereto. The prediction unit 112 may predict the possibility of the useroverturning and falling based on the image captured by the camera device20 without obtaining the plurality of feature amounts. For example, anecessary feature amount may be acquired from the image captured by thecamera device 20 using a learning model created using a convolutionalneural network (CNN). The prediction unit 112 may output the possibilityof the overturning and falling using the learning model created from theimage captured by the camera device 20 using the convolutional neuralnetwork (CNN) or a recurrent neural network (RNN).

As the risk of overturning and falling, for example, the probability ofthe risk that the user overturns or falls and/or a time point at whichthe user overturns or falls (predicted future time point) is output.

The prediction unit 112 may output the risk of overturning and fallingas a level instead of the probability of the risk as the possibility ofoverturning and falling. For example, the prediction unit 112 may outputthat the risk is high when the probability is equal to or greater than athreshold value (for example, 50% or more), and that the risk is lowwhen the probability is less than the threshold (for example, less than50%). The prediction unit 112 may output a plurality of levels such asan overturning and falling risk being “high”, “slightly high”, “slightlylow”, and “low” by providing a plurality of threshold values.

The threshold value for determining the risk by the prediction unit 112may be set by the staff or the like in common, or may be set by thestaff or the like for each user. The controller 100 may appropriatelychange the value according to the state of the user.

It is assumed that the present specification includes the case that theprediction unit 112 outputs the risk of overturning and falling with theprobability and the case that the prediction unit 112 outputs the riskwith the level. Even when it is described that the prediction unit 112outputs the risk of overturning and falling with the probability, theprediction unit 112 may output the risk with the level. Even when it isdescribed that the prediction unit 112 outputs the risk of overturningand falling with the level, the prediction unit 112 may output the riskwith the probability.

The prediction unit 112 can output a time point or a time when the riskof overturning and falling is high. For example, the prediction unit 112can output that the risk of overturning and falling is high at 6 o'clockin the morning, and can output that the risk of overturning and fallingis high after 5 minutes or 15 seconds from the current time point.

Hereinafter, an operation of prediction processing executed by theprediction unit 112 will be described with reference to FIG. 7 .

First, the prediction unit 112 acquires the overturn assessment from thedisease information acquisition unit 110 (step S1300). The overturnassessment includes, for example, (1) an age of the user, (2) a pastmedical history of the user, (3) a degree of physical functionaldisorder of the user, (4) a degree of mental functional disorder of theuser, (5) an activity situation of the user, (6) information on drugsadministered to the user, (7) an excretion situation of the user, (8) atype of a sensor used by the user and setting information thereof, and(9) a type of a fence used by the user and an installation number of thefence.

The prediction unit 112 evaluates a degree of potential overturning andfalling risk for each user based on the overturn assessment (stepS1301).

The prediction unit 112 predicts the overturning and falling risk of theuser based on the potential overturning and falling risk for each userand the information acquired from the camera device 20 or the sensordevice 30 (step S1302 to step S1312). This will be described in detailbelow.

The prediction unit 112 specifies a position of the user (step S1302).The position of the user indicates a place where the user is relativelypositioned with respect to the bed device 3 or a place where the user ispositioned in the living room or the hospital room.

In the present embodiment, it is assumed that the position of the userindicates a relationship between the user and the bed device 3. Forexample, the following three cases are considered as the position of theuser.

First Position: the user is inside the bed device 3. That is, the useris in a lying position, a half sitting position, and a long sittingposition (posture).

Second Position: the user is outside the bed device 3. For example, theuser is not limited to the standing state (posture). The user may usethe wheelchair, the portable toilet, or the like. For convenience ofdescription, in the following embodiment, a state in which the user isstanding will be described as an example.

Third Position: the user is at an end of the bed device 3. That is, theuser is in a sitting position (posture).

The position of the user may be determined, for example, by analyzingthe image of the camera device 20, or may be determined according to adetection result of the sensor device 30. For example, it may bedetermined whether the user is at the second position or the firstposition using a bed-departure and on-bed sensor provided in the beddevice 3. The position of the user may be detected by the load sensorprovided in the bed device 3, and the first position or the thirdposition may be determined.

Subsequently, the prediction unit 112 determines a feature amount to beused according to the position of the user (step S1304). Then, theprediction unit 112 acquires the feature amount determined in step S1304from the acquisition unit (the surrounding environment acquisition unit102, the posture acquisition unit 104, the motion acquisition unit 106,the biological information acquisition unit 108, and the diseaseinformation acquisition unit 110) or from the sensor device 30 (stepS1306).

Subsequently, the prediction unit 112 determines whether there is areduction factor (step S1308). Here, when it is determined that there isa reduction factor, the prediction unit 112 acquires the reductionfactor (step S1308; Yes to step S1310). The prediction unit 112 mayacquire the reduction factor from the acquisition unit, or may acquirethe reduction factor from the image captured by the camera device 20.

The prediction unit 112 executes risk prediction processing (stepS1312). When the risk prediction processing is executed, the predictionunit 112 predicts and outputs a risk for the user. In the presentembodiment, the prediction unit 112 predicts (outputs) the risk ofoverturning and falling as the objective variable by using theprediction dictionary DB 1230, which is learned data, for example, withthe acquired feature amount and the reduction factor as the explanatoryvariables. That is, in the present embodiment, the risk of overturningand falling is output as the risk for the user.

In FIG. 7 described above, the prediction unit 112 executes step S1302after executing step S1300 and step S1301, whereas the presentembodiment is not limited to this case. For example, step S1302 and thesubsequent steps may be executed without executing step S1300 and stepS1301. In this case, instead of the overturn assessment, the predictionunit 112 may acquire information equivalent to the assessment by using,for example, surrounding environment information acquired from theacquisition unit and the sensor device 30 (the surrounding environmentinformation when the camera device 20 or the sensor device 30 is poweredon, or a surrounding environment setting at a timing set by the staff orthe like).

In FIG. 7 , the prediction unit 112 determines the feature amount to beused according to the position of the user (step S1304) and acquires thedetermined feature amount (step S1306), whereas the present embodimentis not limited thereto. For example, the prediction unit 112 may acquirean image or the like from the acquisition unit or the sensor device 30before the prediction processing starts. In this case, instead of stepS1304 and step S1306 in FIG. 7 , the prediction unit 112 may change aweighting for each feature amount according to the position of the user(weighted so that a weight of the feature amount to be used isincreased), and then determine whether there is a reduction factor (stepS1308). In other words, this example is different from FIG. 7 in that afeature amount other than the determined feature amount is also used.

A specific operation example in a case where the prediction unit 112predicts the risk of overturning and falling by machine learning will bedescribed later.

(7) Correspondence Execution Unit

The correspondence execution unit 114 executes an instruction accordingto the output of the prediction unit 112 to, for example, each device.For example, the correspondence execution unit 114 instructs each of thenotifying unit 140, the terminal device 52, and the mobile terminaldevice 54 to appropriately perform notification as necessary, orinstructs the display 130 to output advice.

An operation of correspondence execution processing executed by thecorrespondence execution unit 114 will be described with reference toFIG. 8 . First, the correspondence execution unit 114 determines whetheror not the risk is equal to or greater than a predetermined thresholdvalue. In the present embodiment, the correspondence execution unit 114determines whether or not the probability of the risk that causes theoverturning and falling (hereinafter, simply referred to as“probability”) is equal to or greater than the predetermined thresholdvalue. Then, when the probability is equal to or greater than thethreshold value, the correspondence execution unit 114 executesnotification processing (step S1402; Yes to step S1404).

For example, when the probability is equal to or greater than thepredetermined threshold value (preferably 40% to 60%), thecorrespondence execution unit 114 instructs the notifying unit 140 tonotify that the risk of overturning and falling is high. The notifyingunit 140 performs notification in response to the instruction.

The correspondence execution unit 114 may instruct other devices (theterminal device 52 and the mobile terminal device 54) to performnotification via the communicator 150. The correspondence execution unit114 may instructs a device set by the staff or the like to perform thenotification, or may change a device to which the instruction is issuedaccording to the probability or the situation of the user or the staff.

For example, when the probability is equal to or greater than 40%, thecorrespondence execution unit 114 instructs only the notifying unit 140to perform the notification. However, when the probability is equal toor greater than 50%, the correspondence execution unit 114 may instructnot only the notifying unit 140 but also the terminal device 52 and themobile terminal device 54 to perform the notification. When each deviceperforms the notification, the correspondence execution unit 114 mayinstruct only the mobile terminal device 54 of the staff who isavailable or is at a close distance to perform the notification.

As a notification method, the notifying unit 140 may output an alarm ornotify by light emission or vibration. The notifying unit 140 maydisplay a warning on the display 130. For example, the notifying unit140 may announce attention on a layout or may notify the user of thewarning by sound.

Subsequently, when it is necessary to give a control instruction to eachdevice (step S1406), the correspondence execution unit 114 executesdevice control processing (step S1408). The correspondence executionunit 114 may instruct the bed control device 40 to automatically adjustthe floor height of the bed or the angle of the back section by, forexample, executing the device control processing. In addition to that,the correspondence execution unit 114 may instruct, for example, toautomatically turn on foot lights up to a toilet or an exit, instruct toautomatically switch a sensor setting button operation to an inoperablestate, or instruct to automatically change the setting of thebed-departure sensor.

Subsequently, when the advice can be output, the correspondenceexecution unit 114 executes advice output processing using anoverturning and falling DB (step S1410; Yes to step S1412). By executingthe advice output processing, the correspondence execution unit 114 mayautomatically select, for example, equipment according to an overturnrisk level, or may present a living room layout plan to, for example, apredetermined staff or a nearby staff.

Subsequently, when learning processing is executed, the correspondenceexecution unit 114 may execute the learning processing of the predictiondictionary DB 1230 (step S1414; Yes to step S1416). For example, thecorrespondence execution unit 114 instructs the display 130 to displaythat the risk of overturning and falling is high. At this time, when thestaff or the like comes to cope with the situation but there is nopossibility of overturning and falling of the user, the staff or thelike inputs the fact that there is no possibility through the userinterface device 135. On the other hand, when the notification isperformed by the instruction of the correspondence execution unit 114,the staff or the like inputs that there is the actual risk ofoverturning and falling through the user interface device 135.

The information input by the staff and the like is stored in theprediction dictionary DB 1230. The correspondence execution unit 114performs the machine learning and learns the prediction dictionary DB1230 based on the information input by the staff or the like.

[1.2.2 Other Configurations]

The display 130 displays various kinds of information. For example, thedisplay 130 is constituted by a liquid crystal display or an organic ELdisplay. The display 130 may be another display device connected by HDMI(registered trademark) or D-SUB.

The user interface device 135 inputs various operations from the user,the staff, or the like. For example, an input device such as amanipulation remote controller or a nurse call is also included. A touchpanel formed integrally with the display 130 also functions as the userinterface device 135.

The notifying unit 140 performs the notification. For example, a speakercapable of outputting an alarm sound or a device that performs thenotification by light or vibration may be used. The notifying unit 140may perform the notification by sound output of a content to benotified, or by displaying a warning content on the display 130.

The communicator 150 communicates with other devices. For example, a LANinterface connected to a network, a USB connected to other devices, anda short-distance wireless communication unit are included. Thecommunicator 150 may be a communication device connectable to a mobilephone communication network such as LTE/5G.

The camera device 20 is an image capturing device for capturing an imageof a surrounding situation. The camera device 20 is capable of capturinga still image and a moving image.

The sensor device 30 can acquire the biological information of the userand the position and posture of the user by detecting the body motion orthe like of the user. The sensor device 30 may acquire the biologicalinformation by detecting the load using the load sensor provided in thebed device 3.

The bed control device 40 controls the bed device 3. For example, thebed control device 40 implements a back raising motion/back loweringmotion and a foot raising motion/foot lowering motion by controllingeach section of the back section, the seat section, the upper legsection, and the lower leg section. The bed control device 40 can alsochange the floor height of the bed device 3.

The server device 50 stores the electronic medical record DB 502 inwhich the information on the disease of the user is stored. The serverdevice 50 is a server that manages an in-hospital system, and may be,for example, a server of an electronic medical record server or anordering system. In the electronic medical record DB 502 of the serverdevice 50, in addition to basic information such as the name, date ofbirth, and blood type of the user, various pieces of informationnecessary for treatment such as disease history, examination history,surgical information, administration information, precautions, hospitalvisit history, and overturn history are stored. These pieces ofinformation are merely examples, and the server device 50 does notnecessarily need to store and manage the pieces of information. Forexample, the information related to surgery may be managed by a surgerymanagement server (not shown). The information related to administrationmay be managed by an administration server (not shown).

The peripheral device 60 is provided in other device. For example, anIoT module can be mounted on an obstacle such as a wheelchair, a table,or a cane. These obstacles can be taken as the peripheral device 60 tooutput the positions of the obstacles and the like to the control device10.

[1.3 Processing of Prediction Unit]

Here, the prediction processing executed by the prediction unit 112according to the present embodiment will be described.

[1.3.1 Case where User is Inside Bed Device 3]

The case where the user is inside the bed device 3 (the case where theposition of the user is the first position) will be described. That is,this is a case where the user is in a bed rest state (posture at lyingposition) or the like on the bed device 3. The posture of the userincludes not only the posture at the lying position but also the postureat the long sitting position when the user gets up. The posture at thelying position may be any of a lateral position, a dorsal position, aprone position, a lateral position, and the like.

In this case, the prediction unit 112 determines the following points asfeature amounts to be used, and acquires the determined feature amounts(steps S1304 and S1306 in FIG. 7 ).

(1) Motion

The prediction unit 112 acquires, from the motion acquisition unit 106,as the motion of the user, a feature amount regarding (i) whether or notthe user is stretching his/her hand from the bed device 3 to the outsideof the bed device 3, (ii) whether or not the user is riding over a fenceor straddling the fence, (iii) whether or not the user is removing thefence, and (iv) whether or not the user is moving without operating theuser interface device 135 (for example, whether or not the user gets upwithout operating the user interface device 135). Here, as the patternof using the camera device 20 in the system 1, the pattern b, thepattern c, the pattern d, and the like among the patterns described withreference to FIGS. 2A to 2D may be used.

In the present embodiment, the case where the prediction unit 112 usesall of (i) to (iv) as the feature amount is described, whereas thepresent embodiment is not limited thereto, and a part of (i) to (iv) maybe used. For example, the prediction unit 112 may use only number (i)and number (ii) or only number (i) and number (iii) as the featureamounts. The prediction unit 112 may give a lower priority to a highernumber of number (i) to number (iv), and may weight the feature amountsin a descending order of priority. The prediction unit 112 may give ahigher priority to a higher number of number (i) to number (iv), and mayweight the feature amounts in an ascending order of priority.

(2) Reduction Factor

If there is a reduction factor, the prediction unit 112 acquires thereduction factor in the prediction processing (step S1308; Yes to stepS1310). For example, the following may be considered as the reductionfactor.

Regardless of whether or not the user stretches his/her hand from thebed device 3 to the outside of the bed device 3, if the bed device 3used by the user is a low-floor bed, if the center of gravity or thehead of the user is not outside the bed device 3, or if the user has arestraint band, it is determined that there is the reduction factor inthe motion.

Regardless of whether or not the user is riding over the fence orstraddling the fence, if the bed device 3 used by the user is alow-floor bed or if a mat for impact reduction is installed on a floorsurrounding the bed device 3, it is determined that there is thereduction factor in the motion.

Regardless of whether or not the user removes the fence, if the fence isfixed to the bed device 3, it is determined that there is the reductionfactor in the motion.

For example, the prediction unit 112 normally predicts that the risk ishigh when the user stretches his/her hand from the bed device 3 to theoutside of the bed device 3. However, when the bed device 3 is thelow-floor bed, the prediction unit 112 may predict that the risk relatedto the motion is low even when the user stretches his/her hand from thebed device 3 to the outside of the bed device 3.

When the bed device 3 is the low-floor bed, the prediction unit 112 maydetermine that there is the reduction factor, and predict a risk at alevel lower than the risk level when the user stretches his/her handfrom the bed device 3 to the outside of the bed device 3 for the riskrelated to the motion. As a result, the prediction unit 112 may predictthe risk of overturning and falling as a level lower than that in thecase where the user stretches his/her hand from the bed device 3 to theoutside of the bed device 3.

That is, when there is the reduction factor, the prediction unit 112predicts the risk at a level lower than a normally predicted level (lowprobability). For example, when the user stretches his/her hand from thebed device 3 to the outside of the bed device 3, the prediction unit 112normally predicts that the risk of overturning and falling is “high”.However, when there is the reduction factor such as the bed device 3being the low-floor bed, the prediction unit 112 predicts that the riskof overturning and falling is lowered by one level to “medium”. When theprediction unit 112 outputs the risk of overturning and falling with theprobability, the prediction unit 112 may decrease the probability of therisk. For example, when there is no reduction factor, the predictionunit 112 outputs the probability of overturning and falling as “80%”.However, when there is the reduction factor, the prediction unit 112outputs the probability of overturning and falling as “60%”.

In this manner, when there is another reduction factor, the predictionunit 112 outputs the risk at a level lower than the level of the risk ofoverturning and falling that is originally predicted (output), oroutputs the risk at a value lower than the probability of overturningand falling that is originally output.

[1.3.2 Case where User is Outside Bed Device]

The case where the user is at a position outside the bed device 3, forexample, the case where the user stands (the case where the position ofthe user is the second position) will be described. In this case, theprediction unit 112 determines the following points as feature amountsto be used, and acquires the determined feature amounts (steps S1304 andS1306 in FIG. 7 ).

(1) Surrounding Environment

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, (i) whether or not the user interface device 135is at a position within the reach of the hand of the user as a featureamount. The surrounding environment acquisition unit 102 outputs thefeature amount based on a distance between the position of the userinterface device 135 and the position of the hand of the user or thelike.

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, (ii) a degree of time until the staff or the likevisits the user since the user operates the user interface device 135 asa feature amount. For example, the surrounding environment acquisitionunit 102 calculates a time from when the camera device 20 detects thatthe user operates the user interface device 135 to when the cameradevice 20 recognizes the staff or the like, and outputs a feature amountbased on the calculated time.

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, (iii) whether or not the position of the equipmentis appropriate when there is the equipment (for example, a pedestrian orthe portable toilet) as a feature amount. The surrounding environmentacquisition unit 102 calculates and outputs the feature amount relatedto the position of the equipment based on, for example, the distancebetween the equipment and the bed device 3. Here, as the pattern ofusing the camera device 20 in the system 1, the pattern a, the patternd, and the like among the patterns described in FIGS. 2A to 2D regardingthe above (1) (iii) may be used. For the above (1) (i) and (ii), thepattern b, the pattern c, the pattern d, and the like, among thepatterns described with reference to FIGS. 2A to 2D may be used.

In the present embodiment, the case where all of (i) to (iii) are usedas the feature amounts is described, whereas the present embodiment isnot limited thereto, and a part of (i) to (iii) may be used. Forexample, only the number (i) or only number (i) and number (ii) may beused as the feature amounts. A higher priority may be given to a lowernumber of number (i) to number (iii), and the feature amounts may beweighted in a descending order of priority. A higher priority may begiven to a higher number of number (i) to number (iii), and the featureamounts may be weighted in the descending order of priority.

(2) Posture

The prediction unit 112 acquires, from the posture acquisition unit 104,(i) whether or not the center of gravity of the body of the user isbiased as a feature amount. For example, a part of the user may have aparalyzed symptom. As a result, the center of gravity of the body of theuser may be biased. The posture acquisition unit 104 calculates thecenter of gravity of the body based on the posture of the user,calculates the feature amount based on whether the center of gravity isbiased to the left or right or to the front or back, and outputs thefeature amount.

The prediction unit 112 acquires, from the posture acquisition unit 104,(ii) whether or not the user appropriately wears footwear as a featureamount. Here, as the pattern of using the camera device 20 in the system1, the pattern b, the pattern c, the pattern d, and the like among thepatterns described with reference to FIGS. 2A to 2D may be used.

In the present embodiment, the case where all of (i) and (ii) are usedas the feature amounts is described, whereas the present embodiment isnot limited thereto, and a part of (i) and (ii) may be used.

(3) Motion

The prediction unit 112 acquires, from the motion acquisition unit 106,as the motion of the user, a feature amount regarding (i) whether or notthe user is standing without calling the staff or the like on the userinterface device 135 (whether or not the user is standing withoutoperating the user interface device 135), (ii) whether or not the useris standing in an unbalanced state, (iii) whether or not the motion fromthe standing state until the user takes the posture of a sittingposition in the bed device 3 is appropriately performed when the userenters the bed device 3, (iv) whether or not the user is shaking, (v)whether or not the user is trying to remove or removes a supportivedevice, and (vi) whether or not the user is incontinence. Here, as thepattern of using the camera device 20 in the system 1, the pattern b,the pattern c, the pattern d, and the like among the patterns describedwith reference to FIGS. 2A to 2D may be used.

In the present embodiment, the case where all of (i) to (vi) are used asthe feature amounts is described, whereas the present embodiment is notlimited thereto, and a part of (i) to (vi) may be used. For example,only number (i) or only number (i) and number (ii) may be used as thefeature amounts. A higher priority may be given to a lower number ofnumber (i) to number (iii), and the feature amounts may be weighted in adescending order of priority. A higher priority may be given to a largernumber of number (i) to number (iii), and the feature amounts may beweighted in the descending order of priority.

(4) Reduction Factor

If there is a reduction factor, the prediction unit 112 acquires thereduction factor in the prediction processing (step S1308; Yes to stepS1310). For example, the following may be considered as the reductionfactor.

Regardless of whether or not the center of gravity of the body of theuser is biased, the prediction unit 112 determines that, if the usergrasps a stable item (for example, a fixed equipment), there is areduction factor as to whether or not the center of gravity of the bodyof the user is biased. Even if the user does not grasp the stable item,if the bias of the center of gravity of the body of the user is reduced,the prediction unit 112 determines that there is the reduction factor.

Regardless of whether the motion from the standing state until the usertakes the posture of the sitting position in the bed device 3 isappropriately performed when the user enters the bed device 3, theprediction unit 112 determines that, if the mat for impact reduction isinstalled on the floor surrounding the bed device 3, there is areduction factor as to whether or not the motion is performedappropriately.

For these reduction factors, the motion acquisition unit 106 maydetermine a motion serving as the reduction factor, or may output afeature amount indicating the reduction factor. In this manner, theprediction unit 112 outputs the risk at a level lower than the level ofthe originally output risk of overturning and falling, or outputs therisk at a probability lower than the originally output probability ofoverturning and falling.

[1.3.3 Case where User is in Sitting Position in Bed Device 3]

The case where the user is in the sitting position in the bed device 3(the case where the position of the user is the third position) will bedescribed.

In this case, the prediction unit 112 determines the following points asfeature amounts to be used, and acquires the determined feature amounts(steps S1304 and S1306 in FIG. 7 ).

(1) Surrounding Environment

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, (i) whether or not the setting information of thesensor used for the user is set to a level lower than the settinginformation of the sensor of the overturn assessment as a featureamount. Examples of the sensor include a mat sensor placed on a bed orthe floor and a sheet sensor (body motion sensor) placed under themattress 4, and include various sensors.

The setting information of the sensor used for the user being set to thelevel lower than the setting information of the sensor of the overturnassessment means that the actually installed sensor is not appropriatecompared to the setting information of the sensor necessary for theoverturn assessment.

For example, a case where an appropriate sensor is not provided to theuser (an appropriate sensor is not installed or the type is different),and therefore, only the information that can be acquired from the sensorused by the user is insufficient from a viewpoint of the informationthat can be acquired from the sensor of the overturn assessment.Alternatively, a case where no sensor is installed when it is desired todetect the getting up of the user.

Even if the appropriate sensor is provided to the user, a case where anappropriate setting is not made for the user among a plurality ofsettings of the sensor may be included. For example, when the mat sensorfor detecting bed-departure is installed surrounding the bed device 3,or when the sheet sensor is appropriately provided to the user, it isnecessary to set a notification when the sheet sensor detects thegetting up, but it is set to detect when the user departs from the bed.The surrounding environment acquisition unit 102 outputs, as a featureamount, that the level is set to be low based on the setting informationof the sensor of the overturn assessment and the setting information ofthe sensor used by the user.

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, (ii) whether or not the motion to be detected bythe sensor is correctly detected as a feature amount. The surroundingenvironment acquisition unit 102 calculates and outputs a feature amountbased on whether or not respective results match based on a detectionresult of the sensor and a result of the image captured by the cameradevice 20. For example, when the bed-departure of the user is to bedetected by the sensor, but the sensor does not detect even when theuser departs from the bed, and it can be determined from the image ofthe camera device 20 that the user departs from the bed, the surroundingenvironment acquisition unit 102 determines that the motion to bedetected by the sensor is not correctly detected, and outputs, forexample, a flag “1” as the feature amount.

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, (iii) whether or not the type of fence used by theuser is an appropriate type (a type of fence set in the overturnassessment), and whether or not the number of fences used by the user isequal to or greater than the number of fences set in the overturnassessment, as feature amounts. The surrounding environment acquisitionunit 102 calculates and outputs the feature amounts based on whether thenumber of fences used by the user is equal to or greater than the numberof fences set in the overturn assessment, whether the type of fence usedby the user is an appropriate type, and the like based on the set numberand the type of the overturn assessment and the image captured by thecamera device 20.

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, (iv) whether or not the user appropriately wearsthe supportive device as a feature amount.

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, as feature amounts, (v) when there is theequipment (for example, the wheelchair or the table), whether or not theequipment is locked, (vi) whether or not the obstacle (for example, acarrying object of the user himself/herself) is dropped on the floorsurrounding the bed device 3, (vii) whether or not the user isappropriately wearing clothing (for example, whether or not a button ofthe clothing is displaced or has come undone), and (viii) whether or notthe user interface device 135 is arranged at a predetermined position.The surrounding environment acquisition unit 102 may determine whetheror not the user interface device 135 is arranged at the predeterminedposition based on the distance between the position of the userinterface device 135 and the position of the hand of the user.

In the present embodiment, the case where all of (i) to (viii) are usedas the feature amounts is described, whereas the present embodiment isnot limited thereto, and apart of (i) to (viii) may be used. Forexample, only number (i) to number (v) or only number (i), number (iii),and number (v) may be used as the feature amounts. A higher priority maybe given to a lower number of number (i) to number (viii), and thefeature amounts may be weighted in a descending order of priority. Ahigher priority may be given to a higher number of number (i) to number(viii), and the feature amounts may be weighted in the descending orderof priority.

Here, various patterns of using the camera device 20 in the system 1 areconceivable. For example, in order to acquire the above (1) (v) and(vi), the pattern a, the pattern d, and the like among the patternsdescribed with reference to FIGS. 2A to 2D may be used in the system 1.In order to acquire the above (1) (i), (ii), (iii), (iv), (vii), and(viii), the pattern b, the pattern c, the pattern d, and the like amongthe patterns described with reference to FIGS. 2A to 2D can be used inthe system 1.

(2) Posture

The prediction unit 112 acquires, from the posture acquisition unit 104,a feature amount regarding (i) whether or not the user is sitting deeply(positions of a waist and a hip). Here, as the pattern of using thecamera device 20 in the system 1, the pattern b, the pattern c, thepattern d, and the like among the patterns described with reference toFIGS. 2A to 2D may be used.

(3) Motion

The prediction unit 112 acquires, from the motion acquisition unit 106,as the motion of the user, a feature amount regarding (i) whether or notthe user is performing a motion of departing from the bed appropriately(for example, whether or not the user is performing a motion of slidingdown from the bed with a lower body outside the bed device 3 while anupper body of the user is not raised), (ii) whether or not the user istrying to depart from the bed without calling the staff or the like bythe user interface device 135 (whether or not the user is trying todepart from the bed without operating the user interface device 135),(iii) whether or not the user is shaking, (iv) whether or not the useris trying to depart from the bed without wearing the footwear, (v)whether or not the user is trying to put his/her hand on the equipmentin the case where there is the equipment, and (vi) whether or not theuser is trying to pick up an object surrounding the bed device 3 (forexample, an object which is carried by the user himself/herself, anobject on the floor or in a low place, which is a broader concept thanthe obstacles illustrated above).

Here, as the pattern of using the camera device 20 in the system 1, thepattern a, the pattern d, and the like among the patterns described inFIGS. 2A to 2D may be used for (3) (iv), and (vi) described above. Asthe pattern of using the camera device 20 in the system 1, the patternb, the pattern c, the pattern d, and the like among the patternsdescribed in 2A to 2D may be used for (3) (i), (ii), (iii), (iv), and(v) described above.

In the present embodiment, the case where all of (i) to (vi) are used asthe feature amounts is described, whereas the present embodiment is notlimited thereto, and a part of (i) to (vi) may be used. For example,only number (i) and number (ii) or only number (i), number (iii), andnumber (v) may be used as feature amounts. A higher priority may begiven to a lower number of number (i) to number (vi), and the featureamounts may be weighted in a descending order of priority. A higherpriority may be given to a higher number of number (i) to number (iv),and the feature amounts may be weighted in the descending order ofpriority.

(4) Reduction Factor

If there is a reduction factor, the prediction unit 112 acquires thereduction factor in the prediction processing (step S1308; Yes to stepS1310). For example, the following may be considered as the reductionfactor.

-   -   Regardless of whether or not an object (for example, the        carrying object of the user himself/herself) drops on the floor        surrounding the bed device 3, if the user is trying to depart        from the bed without looking at the obstacle, the prediction        unit 112 determines that there is a reduction factor as to        whether or not the obstacle (for example, a carrying object of        the user himself/herself) drops on the floor surrounding the bed        device 3.    -   In a case where there is the equipment, regardless of whether or        not the user is trying to put his/her hand on the equipment, if        the equipment is locked, the prediction unit 112 determines that        there is a reduction factor as to whether or not the user is        trying to put his/her hand on the equipment.

In this manner, when it is determined that there is the reductionfactor, the prediction unit 112 can output the risk at a level lowerthan the level of the originally output risk of overturning and falling,or at a probability lower than the originally output probability ofoverturning and falling.

[1.3.4 Risk Prediction Processing]

Then, the prediction unit 112 executes the risk prediction processingusing the feature amount acquired in step S1306 and the reduction factoracquired in step S1310 as the explanatory variables (step S1312). Byexecuting the risk prediction processing, the prediction unit 112outputs the overturning and falling risk as the objective variable usingthe prediction dictionary DB 1320 based on the explanatory variables.

[1.3.5 Other Reduction Factors]

Other reduction factors may be output. For example, in a case where thestaff or the like is present near the user or in a case where it ispossible to determine that the overturning and falling risk is lowaccording to the situation of the disease, the prediction unit 112determines that there is the reduction factor.

[1.4 Device Control Processing]

Some examples of the device control processing (step S1408 in FIG. 8 ),which is one of the processing executed by the correspondence executionunit 114, will be described.

The controller 100 (the correspondence execution unit 114) can executeprocessing of controlling other devices or the like by executing thedevice control processing. In the device control processing, thecontroller 100 may execute the following processing based on a valueoutput from the prediction unit 112. In the device control processing,the controller 100 may execute the following processing by analyzing theimage captured by the camera device 20.

(1) The controller 100 may stop the currently operating processing orswitch the processing to different processing. For example, when thecontroller 100 executes rotation processing and a rotation operation isbeing performed, when the probability of overturning and falling isequal to or greater than the threshold value (step 31402; Yes), therotation operation may be stopped. When the probability of overturningand falling is equal to or greater than the threshold value (step S1402;Yes), the controller 100 may change the control to delay the rotationoperation being executed or to switch an expansion and contractionpattern of a cell.

For example, the controller 100 may execute the rotation operation byexpanding and contracting a sub-air cell for rotation placed on or belowthe mattress 4. The controller 100 may implement the rotation operationby changing the angle of the section of the bed device 3, or mayimplement the rotation operation by inclining the section.

(2) Furthermore, the controller 100 may not only simply stop therotation operation, but also change the control according to theacquired posture of the user or the like. For example, when it isdetermined that the posture of the user does not change for apredetermined time, the controller 100 may perform the rotationoperation. At this time, although the controller 100 controls thesub-air cell so as to have a preset angle, when it is determined fromthe image received from the camera device 20 that the posture of theuser does not change, an air cell may be expanded or contracted so thatthe angle of change becomes larger. When the controller 100 determinesthat the posture of the user greatly changes from the image receivedfrom the camera device 20, the controller 100 may expand or contract theair cell so that the angle of change due to the rotation operationbecomes small, or may stop the air cell.

(3) The controller 100 acquires the prohibited behavior of the userbased on the disease information acquired from the disease informationacquisition unit 110. Then, as the motion of the user, when theprohibited behavior of the user is detected from the image received fromthe camera device 20, the controller 100 performs an operation ofnotifying the user via the notifying unit 140.

(4) The controller 100 analyzes the behavior of the user from the imagecaptured by the camera device 20. Then, the controller 100 changes ashape of the bed device 3 (section) by controlling the bed controldevice 40 according to the behavior of the user. For example, when thecontroller 100 recognizes that a meal is served to the user from theimage received from the camera device 20, the controller 100 raises theback section to set the back section to a predetermined back raisingangle so as to have a shape appropriate for the user to have the meal.The controller 100 matches the shape of the bed device 3 with the timepoint and controls the shape of the bed device 3 for each predeterminedtime point. At this time, the controller 100 may or may not change theshape of the bed device 3 according to the state of the user recognizedvia the camera device 20 even when the predetermined time point comes.

2. Second Embodiment

A second embodiment will be described. The first embodiment is anembodiment in which various kinds of processing are executed in thecontrol device 10. The second embodiment is an embodiment in whichvarious kinds of processing are executed by a server device. In thepresent embodiment, only portions different from those of the firstembodiment will be described, and components having common functions aredenoted by the same reference numerals, and description thereof will beomitted.

FIG. 9 is a partial diagram illustrating an overview of a system 2according to the second embodiment. In the second embodiment, a controldevice 15 is connected to the network NW. In addition to the cameradevice 20, various devices are connected to the control device 15. Aserver device 70 executes overturning and falling prediction processingand the like. The server device 70 is connected to the network NW.

FIG. 10 is a diagram for illustrating functions according to the secondembodiment. The processing performed by the control device 10 in thefirst embodiment is implemented by the server device 70.

That is, a living room includes a control device 200 including acontroller, and each device is connected to the control device 200. Adisplay 210 corresponds to the display 130 in the first embodiment. Anoperation device 220 corresponds to the user interface device 135 in thefirst embodiment. A notification device 230 corresponds to the notifyingunit 140 in the first embodiment. A communication device 240 correspondsto the communicator 150 in the first embodiment. Each device may beformed integrally with the control device 200 or may be constituted as aseparate device.

In the second embodiment, data acquired by various devices istransmitted to the server device 70. For example, an image captured bythe camera device 20 is transmitted from the control device 200 to theserver device 70. The server device 70 stores the received image in theimage storage area 1240. Similarly, for example, a signal indicating abody motion acquired by the sensor device 30 is also transmitted to theserver device 70.

Then, in the server device 70, the prediction unit 112 predicts aprobability of the risk of overturning and falling based on receivedvarious signals and information. Then, the correspondence execution unit114 executes notification processing or the like on the notificationdevice 230.

As described above, according to the present embodiment, the functionsdescribed in the first embodiment can also be implemented by the serverdevice 70 or a cloud. In the present embodiment, all the functionsimplemented in the first embodiment are implemented by the server device70, whereas only a part of the functions implemented by the controldevice 10 in the first embodiment may be implemented by the serverdevice 70. For example, the biological information acquisition unit 108and the disease information acquisition unit 110 may be implemented bythe control device 200, and other functions may be implemented by theserver device 70.

3. Third Embodiment

A third embodiment is an embodiment in which acquisition of a featureamount by the camera device 20 and acquisition of a feature amount bythe sensor device 30 are combined. Description of configurations commonto the first embodiment and the second embodiment will be omitted, anddifferent configurations will be described below.

In the third embodiment, the sensor device 30 is provided for each ofthe bed device 3, peripheral devices, and equipment as necessary. Then,the controller 100 (the prediction unit 112) can select the acquisitionof the feature amount from the camera device 20 and the acquisition ofthe feature amount from the sensor device 30 as necessary.

For example, the surrounding environment acquisition unit 102 mayacquire a mounting state of the side rail 6, an assistance bar, and thelike based on information from the sensor device 30 instead of thecamera device 20. Specifically, the sensor device 30 is a pressuresensor that detects a pressure applied to a portion having an optionreceiver. In this case, the surrounding environment acquisition unit 102determines whether or not the side rail 6 is mounted based on thepressure detected by the pressure sensor.

The sensor device 30 may be a contact sensor provided in a lower portionof the option receiver. In this case, the surrounding environmentacquisition unit 102 determines whether or not the side rail 6 ismounted by the contact sensor detecting that the side rail 6 is incontact.

The sensor device 30 may be a direction detection sensor provided in theassistance bar. In this case, the direction detection sensor detects adirection of the assistance bar, so that the surrounding environmentacquisition unit 102 determines whether or not the direction of theassistance bar is correct.

The sensor device 30 may be a sensor that detects connection with theboard 5. For example, in a configuration of JP-A-2017-42398 (Filingdate: Aug. 27, 2015, Title of invention: Brake device in bed device), awire that can be energized is used as a brake wire. The sensor device 30detects whether the board 5 is installed on the bed device 3 or lockedbased on an energization state of the brake wire.

In the system 1, the contact sensor may be provided at each place as thesensor device 30. For example, the position of the hand of the user maybe detected by providing the contact sensor on the side rail 6 or theassistance bar. The pressure sensor may be arranged surrounding the beddevice 3 to detect that the foot of the user touches the floor.

In the system 1, a position detection sensor may be provided in theperipheral device (peripheral device 60) or the equipment as the sensordevice 30. The sensor device 30 may detect the position of theperipheral device (peripheral device 60) or the equipment in the livingroom or a hospital room as a relative position with respect to the beddevice 3.

The controller 100 may acquire a floor height of the bed device 3 fromthe bed control device 40.

As described above, when the controller 100 acquires the feature amount,the controller 100 may acquire the feature amount based on the imagecaptured by the camera device 20, or may acquire the feature amount fromthe sensor device 30, the bed control device 40, or the peripheraldevice 60.

Accordingly, it is possible to perform detection with higher accuracy ormore reliably in a case where detection is performed by another devicethan in a case where detection is performed based on the image capturedby the camera device 20. By combining the camera device 20 and thesensor device 30 or the like, it is possible to reduce processing(processing such as image analysis) in the controller 100, and it ispossible to speed up the overall processing.

4. Fourth Embodiment

In a fourth embodiment, an embodiment in which boards 5 having aplurality of positions of the camera device 20 are used in combinationwill be described. Description of configurations common to those of theabove-described embodiments will be omitted, and differentconfigurations will be described below.

FIGS. 11A to 11D are diagrams for illustrating positions of the cameradevice 20 arranged on the board 5. In the board 5 (first board)illustrated in FIG. 11A, one camera device 20 a (camera device 20) isfixedly arranged in a center or in the vicinity of the center.

In the board 5 (second board) illustrated in FIG. 11B, one camera device20 b (camera device 20) is arranged so that it can be moved from a rightend to a left end.

In the board 5 (third board) illustrated in FIG. 11C, two camera devices20 c and 20 d are arranged at the left and right ends or in the vicinityof the ends.

The board 5 (fourth board) illustrated in FIG. 11D is provided withthree camera devices 20. For example, as illustrated in the first board,the camera device 20 a is arranged at the center or in the vicinity ofthe center, and the camera device 20 c and the camera device 20 d arearranged at the left and right ends or in the vicinity of the ends.

By combining the boards 5, the system 1 can appropriately recognize theuser on the bed device 3.

For example, when the first board is used as the head board, the user onthe bed device 3 can be recognized by using the third board as the footboard. By using the second board or the fourth board as the head board,the camera device 20 may not be mounted on the foot board, or the footboard itself may not be mounted on the bed device 3.

5. Fifth Embodiment

A fifth embodiment is an embodiment in which, when a plurality of beddevices 3 are installed, a camera installed in a place other than thebed device 3 used by the user is used.

FIG. 12 is a schematic diagram illustrating an entire living room orhospital room. FIG. 12 shows an example of the hospital room in whichsix bed devices 3 are provided. For example, in the case of a bed device3A used by a target user, the bed device 3A is provided with a cameradevice 20A1 and a camera device 20A2.

The bed devices are also arranged around the bed device 3A. A bed device3B is arranged on a side facing the bed device 3A (lower side in FIG. 12). A bed device 3AR is arranged on the right side of the bed device 3Ain the drawing. A bed device 3AL is arranged on the left side of the beddevice 3A in the drawing.

A bed device 3BR is arranged on the right side of the bed device 3B,which is arranged to face the bed device 3A, in the drawing. A beddevice 3BL is arranged on the left side of the bed device 3B in thedrawing.

Each bed device 3 is provided with cameras as necessary. For example, inthe bed device 3AL, a camera device 20AL1 is provided on the head board,and a camera device 20AL2 is provided on the foot board. A camera device20R capable of capturing an image of the entire hospital room may beprovided on a wall in the room.

The storage unit 120 stores a range (capture range) that can be capturedby each camera device 20 in a table (capturing range table). FIG. 13Ashows an example of a capturing range table 1250.

The capturing range table 1250 stores identification information forspecifying the camera, a position where the camera is installed, and thecapture range.

Here, the capture range may store, for example, coordinates in a casewhere the hospital room is expressed in an XY space, or may be indicatedby an area set in advance. The controller 100 may calculate by analyzingan image captured by the camera device 20, and may store the capturerange based on a calculated result.

For example, as illustrated in FIG. 13B, an area including thesurrounding of the bed device 3A is virtually set. In FIG. 13B, thefollowing areas are set.

-   -   Area R102: an area on the head side outside the bed device 3A    -   Area R104: an area on the left side of the drawing on the head        side (upper body) outside the bed device 3A    -   Area R106: an area on the left side of the drawing on the head        side (upper body) in the bed device 3A    -   Area R108: an area on the right side of the drawing on the head        side (upper body) in the bed device 3A    -   Area R110: an area on the right side of the drawing on the head        side (upper body) outside the bed device 3A    -   Area R112: an area on the left side of the drawing on the foot        side (lower body) outside the bed device 3A    -   Area R114: an area on the left side of the drawing on the foot        side (lower body) in the bed device 3A    -   Area R116: an area on the right side of the drawing on the foot        side (lower body) in the bed device 3A    -   Area R118: an area on the right side of the drawing on the foot        side (lower body) outside the bed device 3A    -   Area R120: an area on the foot side outside the bed device 3A

An area may be virtually set for all of the bed device 3B and other beddevices. In FIG. 13B, only areas R202, R204, R206, R208, and R210necessary for description are denoted by reference numerals.

Here, the controller 100 can acquire a range in which capturing by thecamera device 20A1 provided in the bed device 3A is possible byreferring to the capturing range table 1250. According to the capturingrange table 1250 in FIG. 13A, the range that can be captured by thecamera device 20A1 is the range of the areas R112 to R120 and the areasR202 to R210.

Accordingly, the controller 100 can specify a range in which asurrounding situation is acquired or the posture and the motion of theuser are acquired by using the camera device 20A1. When the range to becaptured by only the camera device 20 of the bed device 3 used by theuser is insufficient, the controller 100 acquires an image incooperation with the camera device 20 provided in another device.

FIG. 14 is an example of an operation flow of camera cooperationprocessing executed by the controller 100. For example, the controller100 specifies the position of the user (step S2002). At this time, arange to be captured is specified as a range necessary for determining arisk necessary for acquiring the posture and the motion of the user(step S2004). The controller 100 may specify a capturing range necessaryfor acquiring the surrounding environment of the user. The controller100 determines whether or not the capturing range specified in stepS2004 can be captured by a currently enabled camera device 20 (stepS2006). Here, the enabled camera refers to the following camera.

-   -   Camera device 20 provided in bed device 3 used by the user    -   Camera device 20 whose power supply is turned on    -   Camera device 20 that is not provided in the bed device 3 but is        already capable of acquiring an image by the controller 100

When the camera device 20 necessary for capturing the capturing rangespecified in step S2004 is not enabled, the controller 100 refers to thecapturing range table 1250 and enables a necessary camera (step S2006;Yes to step 32008).

Enabling the necessary camera includes turning on the power supply ofthe camera device 20 by the controller 100. Enabling the necessarycamera also includes enabling the controller 100 to acquire an imagefrom the camera device 20 provided in another device, for example, thecamera device 20AL1 provided in the bed device 3AL or the camera device20R provided in the hospital room.

As described above, according to the present embodiment, it is possibleto install not only the camera device 20 provided in the bed device 3but also the camera device 20 in consideration of the arrangement of theliving room, the hospital room, and the entire facility. The controller100 can acquire the posture and the motion of the user, and thesurrounding environment from not only the camera installed in the beddevice 3, but also the camera installed in another device incooperation.

It is possible to switch the state of the camera device 20 as necessary.For example, the control device 10 normally turns off the power supplyof the camera device 20. Then, when the position of the user is includedin the vicinity of the bed device 3, the power supply of the cameradevice 20 may be turned on and the state of the camera device 20 may beswitched as enabled.

In this case, for example, when the bed device 3 includes a plurality ofcamera devices 20, only the camera device 20 capable of capturing animage of a position to which the user approaches may be enabled, andother camera devices 20 may be disabled (power supply turned off) inorder to reduce power consumption. The position of the user may bespecified by using the camera device 20 provided in the living room orthe hospital room.

The control device 10 may enable all the camera devices 20 necessary foracquiring the posture and the motion of the user. For example, thecamera devices 20 provided in the bed devices 3 installed adjacent tothe bed device 3 used by the user and the bed device 3 installed facingthe bed device 3 used by the user may be enabled.

An operation example will be described with reference to FIG. 15 . FIG.15 is a diagram illustrating a relationship between the bed device 3Aand the bed device 3B installed to face the bed device 3A. For example,in the bed device 3A, one camera device 20A1 is provided in the vicinityof the center of a head board 5A1. Here, the capturing range of thecamera device 20A1 is set at a position where the bed device 3B arrangedfacing the bed device 3A is included. Similarly, one camera device 20Bis provided in the vicinity of the center of a head board 5B1 of the beddevice 3B. The camera device 20B is installed at a position where thebed device 3A is included in the capturing range.

Then, the system 1 causes the camera device 20A and the camera device20B to cooperate with each other, so that the user on the bed device 3Acan be recognized without arranging the camera device 20 on a foot board5A2 of the bed device 3A. The user on the bed device 3B can berecognized without arranging the camera device 20 on a foot board 5B2 ofthe bed device 3B.

In FIG. 15 , the bed devices 3 facing each other are described as anexample, whereas the camera devices 20 provided in the adjacent beddevices 3 may be cooperated with each other. For example, in FIG. 12 ,the control device 10 of the first bed device 3A determines that theuser has a bed-departure behavior in step S2002 in FIG. 14 .

The bed-departure behavior of the user may be determined by the postureacquisition unit 104 acquiring that the user has reached the posture ofthe sitting position. The motion acquisition unit 106 may determine thatthe user has the bed-departure behavior using a sensor.

Here, the control device 10 further specifies a direction in which theuser departs from the bed. For example, in FIG. 12 , when it isdetermined that the user departs from the bed device 3A on the left sideof the drawing, an area where the bed device 3AL is present is alsoincluded in the capturing range.

In this case, the control device 10 cooperates with the camera of thebed device 3AL. That is, in step S2006 in FIG. 14 , the controller 100of the control device 10 determines that the capturing range can becaptured by the enabled camera.

Here, since it is preferable that the capturing range includes the areain which the bed device 3AL is present, it is necessary that the cameradevice 20AL1 and the camera device 20AL2 are enabled. By enabling thecamera device 20AL1 and the camera device 20AL2, the controller 100 canacquire images of the user and surroundings of the user following themovement of the user.

According to the present system 1, the camera devices 20AL1 and 20AL2provided on the bed device 3AL can be used in cooperation with eachother as compared with a normal case where only the camera device 20A1and the camera device 20A2 provided on the bed device 3A are used. Thatis, the present system 1 can determine various risks based on a widercapturing range than a normal capturing range.

The capturing range, which is a range necessary for the determination ofthe risk described above, may be dynamically changed. For example, FIG.16A illustrates only the bed devices 3A, 3AL, 3B, and 3BL.

At this time, a curtain may be installed surrounding each bed device 3in order to block the field of view of other users, staffs, and thelike. For example, in FIG. 16A, a curtain 3AC is installed around thebed device 3A (dashed line portion). Similarly, a curtain 3ACL isinstalled around the bed device 3AL. A curtain 3BC is installed aroundthe bed device 3B. A curtain 3BLC is installed around the bed device3BL.

The curtain can be freely opened and closed by the user, the staff, orthe like. Therefore, the range that can be captured by the camerachanges in accordance with the opening and closing of the curtain. Forexample, as illustrated in FIG. 16A, when the curtain is not closed, therange that can be captured by the camera device 20A1 is 3 AH.

Here, a state in which the curtain is closed will be described withreference to FIG. 16B. FIG. 16B is a diagram illustrating only the beddevices 3A and 3AL. In this case, the curtain 3AC is closed between thebed device 3A and the bed device 3AL. For example, the range that can becaptured by the camera device 20A1 of the bed device 3A is 3 AH.Therefore, a range on the bed device 3AL side, which is a range beyondthe curtain, cannot be captured, and an obstacle cannot be detected.

For example, when the user of the bed device 3A departs from the bed onthe bed device 3AL side, it is not possible to appropriately capture theimage. Therefore, the controller 100 acquires an image captured by thecamera device 20AL1 of the adjacent bed device 3AL.

Accordingly, the controller 100 can appropriately acquire the image evenin a range blocked by the curtain 3AC.

The controller 100 may obtain the state of the curtain (for example,whether the curtain is closed or the capturing range is restricted) byanalyzing the acquired image or by detecting by a sensor. For example,the controller 100 may acquire the state of the curtain by analyzing theimage acquired by the camera device 20 provided in the bed device 3 orthe image of the living room or the hospital room captured by anothercamera device 20. The controller 100 may acquire the state of thecurtain by providing the sensor on a curtain rail.

6. Sixth Embodiment

A sixth embodiment is an embodiment in which an operation mode of thebed device 3 is switched or the processing is switched according to therisk predicted by the risk prediction processing. In the presentembodiment, the processing in FIG. 8 in the first embodiment is replacedwith processing in FIG. 17 . The same processing in FIGS. 8 and 17 aredenoted by the same reference numerals, and the description thereof willbe omitted.

First, when the risk is less than a threshold value (step S1402; No),the controller 100 determines whether to execute control of the device(step S3002). Here, when the control of the device is executed, thecontroller 100 sets an operation mode for the bed device 3 and executesthe control. The control to be executed by the controller 100 can bevarious operations such as rotation processing, back raising, footlowering, and kind motion.

For example, it is assumed that the bed device 3 is set to execute therotation processing. The setting to execute the rotation processing maybe, for example, when the staff or the user performs an operation toexecute the rotation processing. When the user does not move for apredetermined time or more, the controller 100 may be set to execute therotation processing.

That is, when the risk is low (for example, when possibility ofoverturning and falling is equal to or less than a set probability), thecontroller 100 executes the rotation processing (step S3002; Yes to stepS3004).

When the risk is equal to or greater than the threshold value while therotation processing is being executed (step S3006: No to step S1402;Yes), the rotation processing is stopped in step S1408.

According to the present embodiment, when the controller 100 determinesthat the risk of the user is high while the device is operating, thecontroller 100 appropriately stops the operation. When it is determinedthat the risk of the user is high when the device is operated, thecontroller 100 can implement that the device is not operated while it isdetermined that the risk is high.

In particular, it is effective in processing such as the rotationprocessing in which the risk of overturning and falling is increased bychanging the position of the user.

7. Seventh Embodiment

A seventh embodiment is an embodiment in which a route is predicted asone of movements of a user to determine a risk.

In the present embodiment, in the risk prediction processing of stepS1312 in FIG. 7 , a route along which the user moves together with therisk of the user is predicted. For example, in the example in FIG. 18 ,when the controller 100 (the prediction unit 112) detects that the userP of the bed device 3A departs from the bed in a direction M1 that is atthe left side, the controller 100 predicts a route M2 of the user P.

The prediction unit 112 may perform prediction in accordance with thedirection in which the user P departs from the bed, a time zone, or anormal behavior. For example, since there is a doorway on the left sidein FIG. 18 , it is predicted that the user P walks toward the doorway.

At this time, the controller 100 executes processing according to theroute. For example, as illustrated in FIG. 19 , the controller 100acquires a motion of the user (step S4002). For example, the motionacquisition unit 106 acquires the motion by executing the motionacquisition processing in FIG. 6 .

Subsequently, the prediction unit 112 predicts the route of the userfrom the acquired motion of the user (step S4004). When there is a riskon the predicted route, the controller 100 executes processingcorresponding to the risk (step S4006; Yes to step S4008).

Although the prediction unit 112 predicts the route of the user in stepS4004, the prediction unit 112 may predict only the place at which thenext motion is to be performed. For example, in FIG. 18 , it is assumedthat when the controller 100 recognizes the user by using the cameradevice 20A1, the user is also recognized by the camera device 20AL1. Inthis case, the controller 100 can acquire that the user is moving in theleft direction in FIG. 18 .

Here, the following processing may be considered as the riskcorrespondence processing.

(1) Case where Obstacle is Present

For example, when there is an obstacle on the route, it may bedetermined that the risk is high. That is, the controller 100 may outputthe risk output by executing the risk prediction processing at a higherlevel than usual. The presence of the obstacle includes, for example, astate in which the floor is wet.

When there is the obstacle, the controller 100 may notify the user thatthere is a risk. When there is the obstacle, the controller 100 maynotify the staff that there is bed-departure or there is the obstacle onthe route.

(2) Brightness Control

The controller 100 may control the brightness for the user on the route.For example, it is assumed that the bed device 3 is provided with anillumination device such as a foot lamp.

For example, the bed device 3A in FIG. 18 is provided with a light 22ALon the left side of the drawing, a light 22AR on the right side of thedrawing, and a light 22AF on the foot side. Similarly, the bed device3AL is provided with a light 22ALL on the left side of the drawing, alight 22ALR on the right side of the drawing, and a light 22ALF on thefoot side.

At this time, since the user P departs from the bed in the M1 direction,the controller 100 turns on the light 22AL. The controller 100cooperates with the control device of the bed device 3AL (or cooperateswith the illumination device) to turn on the light on the route M2.Here, the lights 22ALR and 22ALF of the bed device 3AL are turned on.

At this time, the controller 100 may also turn on a light 22BLF of thebed device 3BL. In this way, the controller 100 can adjust thebrightness on the route in cooperation with the illumination deviceprovided in another bed device 3.

The controller 100 may turn on the illumination device in advance basedon the predicted route, or may turn on the illumination device when theuser P approaches the vicinity of the illumination device.

8. Eighth Embodiment

An eighth embodiment is an embodiment in a case where notification isperformed at an appropriate timing based on a risk other thanoverturning and falling as a risk of a user, or advice is provided to astaff or the like or the user. In the present embodiment, the predictionprocessing in FIG. 7 is replaced.

In the prediction processing in FIG. 7 , after a position of the user isspecified in step S1302, a feature amount to be used is appropriatelydetermined in step S1304. In the present embodiment, a necessary featureamount is determined regardless of the position of the user (that is,without executing step S1302).

The following processing is executed according to, for example, the riskobtained in the risk prediction processing (step S1312).

[8.1 Hand Washing]

The controller 100 (surrounding environment acquisition unit 102)acquires a feature amount based on a motion of the staff or the likesurrounding the user. Specifically, the camera device 20 captures animage of the staff other than the user and acquires the motion of thestaff.

Here, information for specifying the staff (for example, a staff name,and a staff code) may be recognized based on the image captured by thecamera device 20. Information for specifying the staff may be registeredwhen the staff performs a treatment on the user via the terminal device52 surrounding the bed device 3. As a method of registering informationfor specifying the staff, the staff ID or the staff name may be simplyselected by the terminal device 52, or information recorded in an IDcard or the like may be read and registered by a card reader or thelike.

When the controller 100 acquires information indicating that the staffwashes his/her hands, the controller 100 may store the information inthe storage unit 120. The controller 100 may transmit the information tothe server device 50. The server device 50 can manage whether or not thestaff has washed the hands.

Here, whether or not the staff washes the hands may be acquired by asensor, or may be recognized based on the image captured by the cameradevice 20. For example, the controller 100 may acquire informationindicating that the the staff washes the hands via a wearable deviceworn by the staff. The controller 100 analyzes the image captured by thecamera device 20 and recognizes the movement of the staff. Then, thecontroller 100 may acquire the information indicating that the staffwashes the hands from the recognized movement of the staff.

The information stored in the storage unit 120 or transmitted to theserver device 50 by the controller 100 may include, for example, thestaff name (staff ID), a time point at which the staff washed the hands,and a washing place.

Then, when a certain period of time elapses since the staff or the likewashed the hands, the controller 100 may notify the staff that he/shehas not washed the hands. For example, when the controller 100recognizes that the staff or the like is approaching the bed device 3 ofanother user even though a certain period of time elapses since thestaff or the like washed the hands, the controller 100 may notify thestaff by displaying on the terminal device 52 surrounding the bed device3. The controller 100 may notify the terminal device 52 used by thecorresponding staff of the fact.

The controller 100 may perform the notification in accordance with notonly the time but also the state of the user. For example, when the userwho is treated by the staff is involved in infection, the controller 100may notify the staff or the like before and after the treatment.

As described above, according to the embodiment described above, byperforming the notification based on the hand washing, it is possible toreduce the risk of infection such as infection with the staff orinfection with the user.

When the camera device 20 is used, the controller 100 can acquire andnotify information indicating whether or not the staff or the like haswashed the hands in conjunction with the camera device 20 without usingan additional device or a sensor.

For example, the controller 100 can acquire, from the camera device 20,whether or not a family member or a person who visits the hospital tosee the user other than a doctor or a medical staff washes his/herhands.

[8.2 Equipment Management]

The controller 100 (the surrounding environment acquisition unit 102)acquires a feature amount based on a position of the equipment(including a movement auxiliary tool), presence or absence of use of theequipment, and presence or absence of wiping of the equipment.

Then, the controller 100 may store the acquired position of theequipment, the presence or absence of use, and the presence or absenceof wiping in the storage unit 120. The controller 100 may transmitinformation including the position of the equipment, the presence orabsence of use, and the presence or absence of wiping to the serverdevice 50.

The server device 50 may acquire and manage a use situation and anarrangement situation of the equipment in the facility transmitted fromthe control device 10. When the server device 50 receives thenotification that the staff brings the equipment, the server device 50can also notify the staff of the position of the equipment that is closeto the position of the staff and is not used.

The staff can grasp more appropriate equipment by grasping the usesituation of the equipment in the facility. Accordingly, it is possibleto quickly provide the user with necessary equipment, and it is possibleto avoid, for example, a risk of slowing down the correspondence to theuser.

[8.3 Movement of Bed Device]

The controller 100 (the surrounding environment acquisition unit 102)acquires a feature amount based on the state of the bed device 3. Then,the controller 100 determines whether or not the state of the bed device3 is a state that causes a risk, and appropriately notifies the fact orgives advice to the staff or the like. For example, after the bed device3 is moved, the controller 100 performs the notification when the staffdoes not take an appropriate behavior.

For example, it is assumed that a power supply to the control device 10is switched from an AC power supply to a battery power supply in orderto move the bed device 3. The surrounding environment acquisition unit102 acquires the surrounding environment based on the image captured bythe camera device 20.

Here, when the bed device 3 does not move after the power supply isswitched to the battery power supply, the surrounding environmentacquisition unit 102 determines that the switching to the AC powersupply is forgotten (a power supply adapter is forgotten to be insertedinto an outlet), and notifies the staff or the like.

Here, some methods can be considered for determining that the bed device3 does not move. For example, the controller 100 determines that the beddevice 3 does not move when the image captured by the camera device 20is not changed or when a rate of change is small. When there is no orlittle change in a value (acceleration) acquired by the sensor device 30(for example, an acceleration sensor), the controller 100 determinesthat the bed device 3 does not move.

As described above, it is possible to determine, as the risk, that thestaff does not switch the power supply from the battery power supply tothe AC power supply even though the staff finishes moving the bed device3, and to perform notification via the notifying unit 140.

[8.4 Equipment Remaining Amount]

The controller 100 (the surrounding environment acquisition unit 102)determines that there is the risk when equipment around the bed device 3is in a predetermined situation.

The controller 100 acquires a remaining amount of infusion attached toan IV stand/IV pole as a feature amount based on the image captured bythe camera device 20. Here, when it is determined that the amount ofinfusion is equal to or less than a threshold value, it may be notifiedthat the risk is high. The controller 100 may also notify a type ofinfusion to be supplemented at this time. The controller 100 may notifythe staff in charge of the user that the remaining amount of infusion issmall.

[9. Modification]

Although the embodiments have been described in detail with reference tothe drawings, the specific configuration is not limited to theembodiments, and a design or the like within a scope not departing fromthe gist of the present embodiment is also included in the scope of theclaims.

A program that operates in each device in the embodiments is a programthat controls a CPU or the like (a program that causes a computer tofunction) so as to implement the functions of the embodiments describedabove. Information handled by these devices is temporarily stored in atemporary storage device (for example, RAM) at the time of processing,and then stored in storage devices such as various ROMs, HDDs, and SSDs,and read, modified, and written by the CPU as needed.

When the program is distributed to the market, the program may be storedin a portable recording medium and distributed, or may be transferred toa server computer connected via a network such as the Internet. In thiscase, it goes without saying that the storage device of the servercomputer is also included in the present embodiment.

The embodiments described above can also be implemented by, for example,a smartphone or a tablet. For example, a staff or the like places thesmartphone on the foot board of the bed device 3, and captures an imageof the bed device 3 with a camera built in the smartphone. The sensordevice 30, the bed control device 40, and the like acquire informationby being connected by short-distance wireless communication.

By installing an application capable of implementing the functionsimplemented by the controller 100 in the smartphone, it is possible toimplement the system in the above-described embodiments on thesmartphone.

In the above-described embodiments, the notification processing includesvarious methods as processing of notifying the user and/or the staff.For example, the controller 100 may notify that there is a risk bydisplaying the risk on the display 130. The controller 100 may performnotification by using notification methods such as alarm sound, sound,light, and vibration from the notifying unit 140. The controller 100 maynotify other terminal devices via the communicator 150. The controller100 may perform notification via a nurse call system via thecommunicator 150. That is, the control device 10 outputs an alert to theuser or a person other than the user.

Here, the display 130 and the notifying unit 140 through which thecontroller 100 performs notification may be provided in another device.For example, the notification including the display may be performed inthe terminal device on the bed device 3 side, the terminal device in anurse station, or the terminal device that can be confirmed by a doctor.The terminal device may be a stationary device or a portable device. Theportable device may be, for example, a telephone including a smartphoneor the like, or an information terminal device such as a tablet or thelike.

The terminal device to be notified may be switched according to a degreeof risk predicted by the prediction unit 112. For example, when thecontroller 100 determines that the risk is extremely high (theprobability of overturning and falling is higher than a first thresholdvalue), the controller 100 notifies a first terminal device (forexample, the mobile terminal device 54) that the staff can see and asecond terminal device on the bed device 3 side. When it is determinedthat the risk is high (the probability of overturning and falling ishigher than a second threshold value but lower than the first thresholdvalue), the controller 100 notifies only the second terminal device. Inthis manner, the controller 100 may switch a notification destinationdepending on the degree of the risk.

In the above-described embodiments, each piece of information may beacquired from an image captured by the camera device 20 using a learnedmodel. Specifically, the camera device 20 recognizes the motion and thestate of the object using the model. For example, the surroundingenvironment acquisition unit 102, the posture acquisition unit 104, themotion acquisition unit 106, and the biological information acquisitionunit 108 each acquire necessary information using the image captured bythe camera device 20 and the learned model.

For example, the controller 100 inputs the image (signal) captured bythe camera device 20 to a neural network including a plurality of layersand neurons included in each layer. Each neuron receives a signal from aplurality of different neurons and outputs the calculated signal to theother plurality of neurons. When the neural network has a multilayerstructure, the layers are referred to as an input layer, an intermediatelayer (hidden layer), and an output layer in an order in which thesignals flow.

A neural network whose intermediate layer includes a plurality of layersis referred to as a deep neural network, and a method of machinelearning using the deep neural network is referred to as deep learning.A convolutional neural network having a convolution operation provideshigh accuracy in image recognition.

An image is subjected to various operations (convolution operation,pooling operation, normalization operation, matrix operation, and thelike) on the neurons in each layer of the neural network, flows whilechanging the shape, and a plurality of signals are output from theoutput layer.

A plurality of output values from the neural network correspond to theposition of the object, the motion of the user, the posture of the user,and the biological information of the user based on a largest outputvalue. The controller 100 recognizes various kinds of information fromthe image captured by the camera device 20 by using the output value.The output value from the neural network may be recognized from theoutput of a classifier by passing the plurality of output values throughthe classifier without being directly associated with the operation orthe like.

The controller 100 may learn parameters that are coefficients used forvarious calculations of the neural network. For example, the controller100 inputs a large number of images to the neural network in advance,and labels indicating what the object and the state of the user capturedin the image are. Then, the controller 100 may learn an error betweenthe output value and a correct value by propagating the error throughthe neural network in a reverse direction by an error back propagationmethod (back propagation) and updating the parameters of the neurons ineach layer many times.

In this case, even if the sensor device 30 is not connected, the controldevice 10 can acquire appropriate information by using the neuralnetwork using the learned model with only the image captured by thecamera device 20.

Apart from the above embodiments, the prediction processing executed bythe prediction unit 112 may be changed as follows. An example of a casewhere the prediction processing is executed will be described withreference to FIG. 20 .

[User Inside Bed Device 3]

In this case, the prediction unit 112 determines the following points asfeature amounts to be used, and acquires the determined feature amounts(steps S1304 and S1306 in FIG. 7 ).

(1) Surrounding Environment

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, a state of the side rail 6 as a feature amount.The state of the side rail 6 is determined based on whether or not theside rail 6 is installed, the type of the side rail 6, the installationplace of the side rail 6, and the like. The prediction unit 112acquires, from the surrounding environment acquisition unit 102, a stateof the board 5 (foot board/head board) as a feature amount. The state ofthe board 5 is determined based on whether or not the board 5 isinstalled. Here, as the pattern of using the camera device 20 in thesystem 1, the pattern b, the pattern c, the pattern d, and the likeamong the patterns described with reference to FIGS. 2A to 2D may beused.

(2) Posture

The prediction unit 112 acquires, from the posture acquisition unit 104,a feature amount for the center of gravity of the user or the positionof the head of the user. Here, as the pattern of using the camera device20 in the system 1, the pattern b, the pattern c, the pattern d, and thelike among the patterns described with reference to FIGS. 2A to 2D maybe used.

(3) Motion

The prediction unit 112 acquires, from the motion acquisition unit 106,feature amounts as to whether or not the user is stretching his/her handand whether or not the user is performing a motion of attaching anddetaching the side rail 6 as a motion of the user. Here, as the patternof using the camera device 20 in the system 1, the pattern b, thepattern c, the pattern d, and the like among the patterns described withreference to FIGS. 2A to 2D may be used.

(4) Reduction Factor

If there is a reduction factor, the prediction unit 112 acquires thereduction factor in the prediction processing (step S1308; Yes to stepS1310). For example, the following may be considered as the reductionfactor.

First, regardless of the state of the side rail 6, when the user issleeping in a correct posture, it is determined that there is areduction factor for the side rail 6.

-   -   Regardless of whether or not the board 5 is installed, when the        user is sleeping in the correct posture or the side rail 6 is        appropriately installed, it is determined that the risk caused        by the absence of the board 5 is low.    -   Even when a motion that the user stretches his/her hand is        detected as the motion of the user, when a surrounding object        (for example, an object grasped by the user) is close or the        user is sleeping in the correct posture, it is determined that        there is a reduction factor for the motion of the user (the        motion that the user stretches his/her hand).        [Case where User is Standing]

In this case, the prediction unit 112 determines the following points asfeature amounts to be used, and acquires the determined feature amounts(steps S1304 and S1306 in FIG. 7 ).

(1) Surrounding Environment

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, a state of the casters 7 as a feature amount. Thestate of the casters 7 is determined based on, for example, a directionof the casters 7, or a lock state. The prediction unit 112 acquires,from the surrounding environment acquisition unit 102, a state of theequipment surrounding the bed as a feature amount. The state of theequipment surrounding the bed is determined based on the presence orabsence, the type, and the like of the equipment arranged surroundingthe bed device 3. Here, as the pattern of using the camera device 20 inthe system 1, the pattern a, the pattern d, and the like among thepatterns described in FIGS. 2A to 2D may be used.

(2) Motion

The prediction unit 112 acquires, from the motion acquisition unit 106,feature amounts as to whether the user is shaking, whether the user isperforming a motion of removing the side rail 6, or whether the user iswalking with socks (slippers) as the motion of the user. Here, as thepattern of using the camera device 20 in the system 1, the pattern b,the pattern c, the pattern d, and the like among the patterns describedwith reference to FIGS. 2A to 2D may be used. Whether the user iswalking with socks (slippers) may be determined by using the pattern a,the pattern d, and the like among the patterns described in FIGS. 2A to2D in the system 1.

(3) Reduction Factor

If there is a reduction factor, the prediction unit 112 acquires thereduction factor in the prediction processing (step S1308; Yes to stepS1310). For example, the following may be considered as the reductionfactor.

Regardless of the state of the casters 7, when a standing posture of theuser is appropriate, a transfer destination to which the user transfersis locked, or an object that the user grasps is appropriately arranged,it is determined that there is the reduction factor for the casters 7.

In this manner, when it is determined that there is the reduction factorfor the casters 7, the prediction unit 112 outputs the risk at a levellower than the level of the originally output risk of overturning andfalling, or at a probability lower than the originally outputprobability of overturning and falling.

[Case where User is in Sitting Position in Bed Device 3]

In this case, the prediction unit 112 determines the following points asfeature amounts to be used, and acquires the determined feature amounts(steps S1304 and S1306 in FIG. 7 ).

(1) Surrounding Environment

The prediction unit 112 acquires, from the surrounding environmentacquisition unit 102, the floor height of the bed device 3 (a section ofthe bed device 3 or a ground height of the mattress 4) as a featureamount. The prediction unit 112 acquires, from the surroundingenvironment acquisition unit 102, a state of the assistance bar as afeature amount. The prediction unit 112 acquires a state of equipmentsurrounding the bed device 3 as a feature amount. The state of theequipment surrounding the bed device 3 is determined based on, forexample, the type of the equipment, the state of the equipment, and theposition of the equipment. The prediction unit 112 acquires a state of asensor provided in the bed device 3 as a feature amount. The state ofthe sensor is determined based on, for example, the presence or absenceof installation of the sensor, the type of the sensor, and a valueoutput from the sensor.

Here, various patterns of using the camera device 20 in the system 1 areconceivable. For example, in order to acquire the floor height of thebed device 3 and the state of the equipment surrounding the bed device3, the pattern a, the pattern d, and the like among the patternsdescribed with reference to FIGS. 2A to 2D may be used in the system 1.In order to acquire the state of the assistance bar, the pattern b, thepattern c, the pattern d, and the like among the patterns described withreference to FIGS. 2A to 2D can be used in the system 1. In order toacquire the state of the sensor, the pattern a, the pattern b, thepattern c, the pattern d, and the like among the patterns described withreference to FIGS. 2A to 2D can be used in the system 1.

(2) Posture

The prediction unit 112 acquires, from the posture acquisition unit 104,feature amounts for the position of the hand of the user, as to whetheror not the foot of the user is on the floor, and whether or not the useris sitting deeply (positions of the waist and the hip). Here, as thepattern of using the camera device 20 in the system 1, the pattern b,the pattern c, the pattern d, and the like among the patterns describedwith reference to FIGS. 2A to 2D may be used. In order to acquirewhether or not the foot of the user is on the floor, the pattern a, thepattern d, and the like among the patterns described with reference toFIGS. 2A to 2D may be used in the system 1.

(3) Motion

The prediction unit 112 acquires, from the motion acquisition unit 106,feature amounts as to whether or not the user is carrying out the motionof wearing footwear, whether or not the user is trying to pick up anobject at a distant place, and whether or not the user is carrying outthe motion of removing the side rail 6 as the motion of the user. Here,as the pattern of using the camera device 20 in the system 1, for themotion of the user wearing the footwear, the pattern a, the pattern d,and the like among the patterns described with reference to FIGS. 2A to2D may be used. As the pattern of using the camera device 20 in thesystem 1, for the motion of whether or not the user is trying to pick upthe object at the distant place, the pattern d or the like among thepatterns described with reference to FIGS. 2A to 2D may be used. As thepattern of using the camera device 20 in the system 1, for the motion ofremoving the side rail 6, the pattern b, the pattern c, the pattern d,and the like among the patterns described with reference to FIGS. 2A to2D may be used.

(4) Reduction Factor

If there is a reduction factor, the prediction unit 112 acquires thereduction factor in the prediction processing (step S1308; Yes to stepS1310). For example, the following may be considered as the reductionfactor.

-   -   Regardless of the state of the assistance bar, when the posture        of the user at the sitting position is appropriate, it is        determined that there is a reduction factor for the state of the        assistance bar.    -   Regardless of the state of the equipment surrounding the bed        device 3, when the posture of the user at the sitting position        is appropriate, it is determined that there is a reduction        factor for the state of the equipment surrounding the bed device        3.    -   Regardless of the state of the position of the hand of the user,        when the object surrounding the bed device 3 is close and the        user is sleeping in the appropriate posture, it is determined        that there is a reduction factor for the position of the hand.    -   Regardless of the motion that the user tries to pick up the        object at the distant place, when the object surrounding the bed        device 3 is close and the user is sleeping in the appropriate        posture, it is determined that there is a reduction factor for        the motion that the user tries to pick up the object at the        distant place.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of the disclosures. Indeed, the novel embodiments described hereinmay be embodied in a variety of other forms; furthermore, variousomissions, substitutions and changes in the form of the embodimentsdescribed herein may be made without departing from the spirit of thedisclosures. The accompanying claims and their equivalents are intendedto cover such forms or modifications as would fall within the scope andspirit of the disclosures.

What is claimed is:
 1. A bed system comprising: an imaging device; a bed on which the imaging device is to be installed; and a controller configured to process an image acquired by the imaging device to predict a possibility of falling of a user from the bed, wherein when it is determined, by the controller, that a state of the user is a first state, the controller predicts the possibility of falling of the user based on a first parameter, when it is determined, by the controller, that the state of the user is a second state, the controller predicts the possibility of falling of the user based on a second parameter, the first state is a state of the user different from the second state, the first parameter is different from the second parameter, the first parameter includes whether or not a side rail of the bed is installed, and a position of a head or a posture of the user, the first parameter further includes a center of gravity of the user, whether or not the user is removing a fence, or whether or not the user is lowering a fence, and the second parameter includes a state of casters of the bed, a state of equipment surrounding the bed, whether or not the user is shaking, whether or not the user is removing the fence or lowering the fence, and whether or not the user is walking with socks or slippers.
 2. The bed system according to claim 1, wherein the state of the user is a position of the user.
 3. The bed system according to claim 2, wherein the position of the user is any one of inside of the bed, outside of the bed, and an end of the bed.
 4. The bed system according to claim 1, wherein the state of the user is a posture of the user, and the posture of the user is any one of a lying position, a sitting position, and a standing position.
 5. A bed system comprising: an imaging device; a bed on which the imaging device is to be installed; and a controller configured to process an image acquired by the imaging device to predict a possibility of falling of a user from the bed, wherein when it is determined, by the controller, that a state of the user is a first state, the controller predicts the possibility of falling of the user based on a first parameter, when it is determined, by the controller, that the state of the user is a second state, the controller predicts the possibility of falling of the user based on a second parameter, the first state is a state of the user different from the second state, the first parameter is different from the second parameter, the controller is configured to when there is a reduction factor, predict the possibility of falling of the user based on the first parameter to be lower than when there is no reduction factor, and when there is a reduction factor, predict the possibility of falling of the user based on the second parameter to be lower than when there is no reduction factor, and the reduction factor is a factor indicating a reduction in the possibility of falling of the user, and calculated based on the state of the user, an environmental state, or a state of a caregiver.
 6. A bed system comprising: an imaging device; a bed on which the imaging device is mounted; and a controller configured to process an image acquired by the imaging device to predict a possibility of falling of a user from the bed, wherein the controller is configured to predict a risk based on a parameter selected based on positions of the bed and the user, and when it is determined that there is a reduction factor based on the image acquired by the imaging device, predict the risk at a level lower than a predicted level, and the reduction factor is a factor indicating a reduction in the possibility of falling of the user, and calculated based on the state of the user, an environmental state, or a state of a caregiver.
 7. The bed system according to claim 5, wherein the first parameter includes whether or not a side rail of the bed is installed, and a position of a head or a posture of the user.
 8. The bed system according to claim 7, wherein the first parameter further includes a center of gravity of the user, whether or not the user is removing a fence, or whether or not the user is lowering a fence, and the second parameter includes a state of casters of the bed, a state of equipment surrounding the bed, whether or not the user is shaking, whether or not the user is removing the fence or lowering the fence, and whether or not the user is walking with socks or slippers.
 9. The bed system according to claim 5, wherein the state of the user is a position of the user.
 10. The bed system according to claim 9, wherein the position of the user is any one of inside of the bed, outside of the bed, and an end of the bed.
 11. The bed system according to claim 5, wherein the state of the user is a posture of the user, and the posture of the user is any one of a lying position, a sitting position, and a standing position. 